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LLM Implementation and Maintenance Costs for Businesses: A Detailed Breakdown

Martyna Mul — Wed, 14 May 2025 06:44:35 +0000

When considering the introduction of artificial intelligence into your company, it’s important to understand the costs involved in implementing and maintaining your own LLM. Expenses go beyond just paying for model usage (e.g., token-based API fees) and include a range of factors — from infrastructure to security. Below, we discuss the types of costs associated with using dedicated LLMs and present example calculations for popular models (such as GPT-4, Claude, Mistral, LLaMA, etc.), including business use case scenarios.

More and more companies are considering the use of large language models (LLMs) in their own products and processes. These “dedicated” models can act as intelligent assistants—answering customer questions, analyzing documents, generating reports, and much more. You can read more about it here.

Types of Costs When Using LLMs

Before starting the implementation, it’s important to understand all the components that contribute to the total cost of using a dedicated model.

Infrastructure:

If you’re using models via a cloud API (OpenAI, Anthropic, Google), you only pay for the tokens used. The infrastructure cost is “hidden” on the provider’s side.

If you choose to self-host a model such as Mistral or LLaMA, you’ll need to maintain a GPU server—either locally or in the cloud. For example, renting an instance with an A100 GPU typically costs $1–2 per hour, which amounts to $750–1,500 per month if the server runs continuously. While such an investment can handle a high volume of queries, it may be underutilized at a smaller scale.

Licensing and Model Fees

Commercial models come with licensing or subscription fees. For example, when using the GPT-4 API from OpenAI or Claude from Anthropic, you pay per token used according to the provider’s pricing (we outline token costs in detail later on). On the other hand, open-source models like LLaMA or Mistral are available for free—there are no licensing or token fees. Meta, for instance, released LLaMA 2 under a license that allows businesses to use it freely. However, “free” doesn’t mean zero cost—you’ll still pay for the infrastructure and electricity needed to run the model (as mentioned earlier). It’s also important to check license restrictions: some open models may have specific usage conditions (e.g., restrictions on certain industries).

Model Adaptation and Customization

For an LLM to perform well in a specific company setting, it often requires customization—such as additional training (fine-tuning) on company-specific data or at least the preparation of tailored prompts (known as prompt engineering). This adaptation process can generate significant costs:

- Model Fine-Tuning: Training a model on your own dataset requires computing power (typically GPUs running for many hours) and expert knowledge. For larger models, this can cost anywhere from several thousand to tens of thousands of dollars—factoring in both infrastructure expenses and specialist time. Even fine-tuning a smaller model (e.g., GPT-3.5) via OpenAI’s API can incur significant costs, as it involves processing hundreds of thousands or even millions of tokens during training—billed according to the provider’s token pricing.

- Prompt Engineering: As an alternative or complement to training, you can craft tailored prompts and instructions for the model. While writing prompts itself doesn’t require paid resources, iteratively testing and refining multiple versions consumes tokens (which adds cost when using a cloud-based model) and takes up team time. This can be viewed as either an operational cost or a competence-related expense—specialist time is needed to optimize the model’s behavior for your specific use case.

Operational Costs

After deploying the model, ongoing operational costs come into play. These include monitoring the model’s performance, maintaining efficiency, logging results, applying updates, and fixing potential issues. If you’re using an API, the main operational cost will be the monthly bill for consumed tokens, along with any premium subscription fees (some providers offer subscription plans with usage limits or preferred pricing). If the model is hosted locally, operational costs typically include:

- Electricity consumption – GPU-based models can consume significant amounts of power, leading to substantial monthly energy costs.
- System administration – Time spent by administrators on server maintenance, backups, and updating software components (e.g., AI libraries).
- Infrastructure scaling – As demand grows, additional machines or cloud instances may be needed, resulting in further expenses.
- High availability – If the LLM assistant needs to operate 24/7 without downtime, you may need to invest in redundant resources (e.g., backup servers) or enter into an SLA agreement with your cloud provider.

Team Expertise

Implementing an LLM requires the right expertise within the IT/Data team. If your company lacks AI experience, it may be necessary to train existing employees or hire new specialists—such as an ML engineer or MLOps expert—which adds recruitment or training costs. Alternatively, some companies choose to work with external consultants or service providers to deploy the model. This also incurs costs, usually one-time project fees, which can be significant. It’s also important to account for the time your team spends integrating the model with existing systems (e.g., connecting it to a database or user-facing application). This is a labor cost that’s often overlooked in smaller projects but can have a major impact in practice.

The categories above show that the total cost of owning a dedicated LLM-based solution goes far beyond just the fee for accessing the model. It’s important to consider all these factors before making a decision. In the next section, we’ll look at specific numbers: how much a single prompt costs for various popular models, and what it would take to maintain a simple LLM assistant in two example business scenarios.

Cost of a Single Prompt in Popular LLM Models

Language models are typically billed based on the number of tokens processed. A token is a small piece of text—it may represent a single word or part of a word (for example, 1,000 tokens roughly equals 750 words of continuous text). API providers list prices per 1,000 or 1 million tokens.

Below is a comparison of the approximate cost to process 1,000 tokens using selected popular LLM models:

LLM Model Comparison

LLM Model	Access / License	Cost per 1000 tokens	Notes
GPT-3.5 Turbo (OpenAI)	Cloud API (chat model available, e.g., in ChatGPT)	$0.0015 (input) $0.0020 (output)	Very low cost – 16k tokens + paid upgrade to 128k Good response quality
GPT-4 (8k)	Cloud API (OpenAI)	$0.08 (input) $0.16 (output)	High quality; high cost
GPT-4 Turbo (128k)	Cloud API (OpenAI)	$0.01 (input) $0.03 (output)	Reliable large context (up to 128k tokens) Cheaper (only slightly more than GPT-3.5)
Claude Instant v1.2	Cloud API (Anthropic)	$0.0008 (input) $0.0024 (output)	Fast, lower-cost Claude model (equivalent to GPT-3.5)
Claude 2 (100k)	Cloud API (Anthropic)	$0.008 (input) $0.024 (output)	High-quality model by Anthropic; context up to 100k tokens
Mistral 7B	Open source (free model)	Token cost: $0	Requires self-hosting Alternative to GPT-3.5 – low hardware requirements (can run with <1M tokens)
LLaMA 2 13B	Open source (free model)	Token cost: $0	Self-hosting required Needs stronger hardware (e.g., 2× 24GB GPU) than 7B, but still accessible for many companies
LLaMA 2 70B	Open source (free model)	Token cost: $0	Requires self-hosting Requires expensive infrastructure (e.g., 8× 80GB GPUs) At this scale, costs may match or even exceed GPT-4

Legend: How Token Costs Are Calculated

- Input tokens – words contained in the user’s prompt.
- Output tokens – words generated by the model in response (completion).

For most commercial providers, the cost is charged separately for input and output tokens. For example:

GPT-4 Turbo:

- 1,000 input tokens: $0.03
- 1,000 output tokens: $0.06

If a dialogue contains a total of 1,000 tokens (e.g., 500 input + 500 output), the cost is approximately $0.045.

For simplicity, you can assume that a full interaction of 1,000 tokens costs about $0.09.

By comparison:

- GPT-3.5 Turbo – a similar 1,000-token dialogue costs only about $0.0035 (i.e., 0.35 cents).
- Open-source models (e.g., Mistral, LLaMA) – token costs are $0, since the models run locally. You only pay for infrastructure-related costs (power consumption, server uptime, etc.).

Open-source models (such as Mistral, LLaMA, etc.) are attractive because they come with no fees for the model itself—you can generate any number of tokens without paying the model provider a cent. However, to run these models, you need to maintain your own infrastructure. At a small scale, the cost of renting a machine for a single query may actually exceed the cost of an individual API call to a model like GPT. On the other hand, at a large scale—with many queries per day—open-source solutions can become significantly more cost-effective. In summary, cost-effectiveness depends on the use case, which we’ll explore in the next section.

Example Costs of Implementing an LLM Assistant (100 Queries per Day)

Let’s now consider a practical scenario: your company wants to implement a simple LLM-based virtual assistant that performs one of the following tasks:

- Document analysis – e.g., the assistant reads offers or contracts and extracts key information such as clauses, deadlines, and amounts.
- Customer inquiry handling – e.g., the assistant replies to customer emails with questions about pricing, product availability, technical support, etc.

Let’s assume that:

- The assistant will handle approximately 100 interactions per day.
- Each interaction consists of a prompt and a response, totaling around 2,000 tokens (e.g., 1,000 tokens in the prompt—roughly 750 words or several paragraphs—and 1,000 tokens in the response, or about 750 generated words). This token size covers fairly complex queries and detailed replies.
- On a monthly basis, the assistant will process around 6 million tokens (3,000 interactions × 2,000 tokens = 6,000,000 tokens).

We want to compare the monthly operating costs of such an assistant depending on the choice of model and deployment approach. We’ll present two variants:

- API Variant (Closed Model): We use a commercial model via an API (e.g., OpenAI GPT or Anthropic Claude). We don’t maintain our own servers—costs are limited to token usage, billed according to the provider’s pricing.
- Self-Hosted Variant (Open-Source Model): We use an open-source model (e.g., Mistral or LLaMA) deployed on our own servers. Costs include infrastructure needed to support approximately 100 queries per day—such as cloud GPU instance rental or hardware amortization, plus electricity.

Below is a table comparing estimated monthly costs for several example models under both deployment variants, assuming 6 million tokens per month:

Monthly LLM Cost Comparison

Model (variant)	Estimated Monthly Cost	Comment
GPT-3.5 Turbo (API)	approx. $18 (USD)	Very low cost for this quality level. Estimate: approx. $0.0027/1k tokens → $12 for generating 4M tokens + $6 for prompts → ~$18/month total.
GPT-4 (8k) (API)	approx. $270	Much higher cost for better quality. Example: 8M tokens → cost: 8M × $0.08/1k (input) + $0.16/1k (output) → $270–$540 monthly.
GPT-4 Turbo (128k) (API)	approx. $18	Slightly more expensive than GPT-3.5 due to cheaper input/output token pricing. May even deliver better quality than GPT-4 (8k).
Claude Instant (API)	approx. $20–25	Comparable to GPT-3.5 in cost. Estimate: approx. $0.0021/1k tokens (input+output) → ~$18–25 for 8M tokens (plus potential flat fees).
Claude 2 (API)	approx. $150–200	Cheaper than GPT-4, but still several times more expensive than GPT-3.5. Estimate: $0.032/1k tokens → ~$192 for 8M tokens.
Mistral 7B (open source, self-hosted, 1x GPU)	approx. $300	Cost mainly for maintaining server/GPU. Assumption: 1x 24GB GPU instance – model generates ~30–60 tokens/sec, power usage 100–150W. Actual cost depends on location and usage (electricity + server = ~$300–400/month).
LLaMA 2 70B (open source, self-hosted, multi-GPU)	approx. $1,000+	High cost due to powerful GPU requirements. Typically requires at least 8×80GB GPUs (~$10k–12k hardware + high power consumption). Costs vary based on setup model (on-prem / cloud / GPU provider).
Local model (e.g., LLaMA 13B, GPTQ, Mistral 7B – CPU)	approx. $300–500	Cost includes operation of local server. May be slower than GPT-3.5, but offers more privacy and control. For CPU instance (e.g., 12 cores, 64 GB RAM), monthly cost is mainly for electricity and maintenance.

From the above comparison, several key takeaways can be drawn:

Small-scale usage (100 queries/day) favors API solutions

With relatively low query volume, using a commercial API (OpenAI, Anthropic) is highly cost-effective—especially with lower-priced models like GPT-3.5 or Claude Instant, where monthly costs can be as low as a few dozen dollars. For higher-end models, monthly costs may rise to several hundred dollars. Still, at this scale, running your own GPU server at $300+ per month would be less economical than relying on cloud-based APIs.

Large-scale usage (thousands of queries) changes the equation

If your assistant becomes successful and the number of queries increases by 10x or even 100x, the monthly API bill could grow to thousands or even tens of thousands of dollars. In such cases, investing in an open-source, self-hosted model starts to make financial sense. With a high enough query volume, the per-request cost of running the model locally becomes lower than the API cost—since the purchased or rented hardware is being used more efficiently. In extreme cases of massive scale, some organizations may even consider training their own model from scratch—but this is typically reserved for the largest players with very substantial budgets.

Use Case Matters (Quality vs. Cost Efficiency)

Choosing the right model shouldn’t be based solely on cost—it also depends on the quality of output required for your use case. In a document analysis scenario, precision in extracting information is the top priority. A lower-cost or open-source model may be sufficient here, especially if fine-tuned to the task. A model with 7B–13B parameters can offer adequate performance at a much lower cost. Moreover, when processing sensitive documents (e.g., contracts), running the model locally ensures that the content never leaves your organization—an invaluable benefit from a legal and data privacy standpoint. On the other hand, in customer inquiry handling, where natural language quality, politeness, and contextual understanding are critical, GPT-4 can significantly outperform smaller models. In this case, a company may find it worthwhile to pay more for superior customer experience.

Hidden Costs Around the Project

It’s important to note that the above calculations cover only the technical costs—such as token usage or infrastructure. In practice, there are also “soft” costs to consider, including staff time for preparing the implementation, integrating the model with systems like a CRM or knowledge base, testing, and ongoing iterations and improvements. For example, if the assistant needs to retrieve data from a company’s internal document repository, those documents often need to be organized or cleaned before they can be effectively used by the model.

Cost Example: AI Assistant for Analyzing Emails and PDF Documents

Here we also present the cost breakdown of our assistant based on Google’s Gemini model, which we described [here]. Its task is to automatically analyze incoming emails to identify insurance policies and extract key data from attached PDF documents—such as policy number, insured party address, or payment confirmation.

Average Token Count per Email:

- Input: 3,500 tokens
- Output: 220 tokens

Analyzing 100 emails with attachments using the Gemini 2.0 Flash model costs approximately $1.50.

Summary

Can We Afford Our Own “ChatGPT” in the Company? As we’ve seen, the answer is: it depends—primarily on the scale of usage and quality requirements. The key lies in selecting a model and deployment method that aligns with your specific needs. An iterative approach is often the most practical: start with a lower-cost model or API, evaluate the results, and scale up to a more powerful model or self-hosted solution as the project matures. Regardless of the path you choose, careful planning and cost monitoring across all categories is essential. We hope this comparison helps you make informed decisions and prepare a realistic budget for implementing a dedicated LLM in your organization.

If you’re considering implementing an assistant in your company, it’s worth finding answers to the following questions:

- Do I need high-quality responses (e.g., GPT-4), or is an approximate answer sufficient (e.g., Claude Haiku, Gemini Flash)?
- Am I processing sensitive data (e.g., customer documents)?
- Do I have an IT team capable of hosting a model in-house?
- What is the expected number of queries per day/month?
- Is it more cost-effective to maintain my own infrastructure, or should I pay for API access?

For small to medium-scale applications, the cost of using a dedicated LLM can be quite reasonable. Thanks to cloud-based services, it’s possible to get started for just a few dozen dollars per month with models like GPT-3.5 or Claude Instant—an excellent option for experimentation and early prototypes. If you need top-tier performance, such as what GPT-4 offers, you’ll need to account for higher costs. However, even a few hundred dollars per month can be justified if the business value is significant—for example, by automating tasks that would otherwise require many hours of manual work.

On the other hand, for large companies planning intensive AI use, costs can grow exponentially—making it worth considering open-source options and greater investment in in-house infrastructure. Open models like LLaMA or Mistral offer freedom from per-token fees, but shift the cost burden to hardware and staffing. They become cost-effective when operating at scale or when full control over data is a top priority.

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Chatbot, Agent or AI Assistant? Find Out Which Solution Is Best for Your Business

Marta Kuprasz — Thu, 08 May 2025 08:57:21 +0000

Artificial intelligence and Large Language Models are buzzwords heard in nearly every industry. Many companies are wondering how to use them safely and which solution will be the most effective. There are plenty of options—and they’re often hard to tell apart. In this article, we break them down in a clear and easy-to-understand way.

AI can take on many roles in a company—as a chatbot, assistant, agent, data analysis tool, content generator, or knowledge search engine. So how can you choose the solution that best fits your employees’ needs? It helps to understand what each option has to offer.

Chatbot – answers questions, provides explanations, and handles requests

This is the most common use of AI in areas such as customer service and sales. An AI chatbot based on a large language model, such as ChatGPT, can hold natural conversations, understand the context of inquiries, and deliver accurate answers—24/7, in multiple languages, and without human involvement.

These solutions are typically implemented on websites, in messaging platforms (like Messenger or WhatsApp), or within helpdesk systems, where they assist with answering questions, tracking orders, or providing product information. As a result, they significantly automate customer service, reduce operational costs, and improve customer satisfaction ratings.

For the purposes of this article, we define a chatbot as an AI interface primarily intended for external users—in other words, it operates “outside the company.” This definition distinguishes it from AI agents, which perform more complex tasks within internal processes by integrating with systems, databases, or APIs.

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AI Agent – a tool designed to carry out specific tasks

Unlike a chatbot, which interacts with external users, an AI agent operates within the organization and supports employees by automating specific business processes. It’s not a one-size-fits-all tool—it’s built with a clearly defined purpose in mind, such as document processing, data analysis, or integration with ERP systems.

Thanks to large language models like Gemini or Claude, an AI agent can understand context, make decisions, and trigger specific actions—without human input. It can run in the background, process data from multiple sources, manage files, or handle email inboxes. Each AI agent is tailored to the company’s individual needs and specific tasks. Only then can it offer real value instead of becoming just another generic tool.

Want to see how this works in practice?

Check out our case study: Meet your personal AI agent-a case study for a freight forwarding company – where we describe how we built an agent integrated with an email inbox.

AI Assistant – supports users in daily work by operating contextually and “in the background”

Unlike a chatbot that answers questions or an agent that automates a specific process, an AI assistant is a tool that works alongside employees in real time—it understands context, suggests next steps, and makes tasks easier within familiar applications.

It’s typically integrated into a specific work environment, such as a word processor, spreadsheet, CRM, or project management tool. The assistant doesn’t replace the user—it actively supports them in making decisions, writing, analyzing data, or planning.

AI assistants like GitHub Copilot, Notion AI, or Google’s Workspace assistant show how this technology can genuinely boost team productivity and reduce time spent on routine tasks. From a business perspective, a well-designed assistant can improve work quality, reduce errors, and make onboarding new employees easier.

Other Business Applications of Large Language Models

The possibilities go far beyond chatbots, assistants, or agents. These models can take on specialized roles, supporting tasks such as document processing, data analysis, or content creation. They’re increasingly used to automatically summarize reports, extract information from unstructured sources (like emails, PDFs, or scanned forms), or answer natural-language questions based on internal documentation.

LLMs can also assist marketing teams by generating suggestions for ad copy, product descriptions, or sales messages tailored to the company’s style. In analytics departments, they provide faster access to data—generating database queries, interpreting results, and presenting insights in a way that’s easy for non-technical users to understand. These applications often don’t require building a new tool from scratch, but rather integrating the AI model into existing company systems. This way, the technology supports specific tasks—right where it’s needed.

AI Models and Data Security

Business owners and managers still approach AI tools with caution, mainly because they’re unsure how to ensure the security and confidentiality of processed data. We’ve explored these topics in previous publications that are worth reviewing.

In the article “AI User Privacy: An Analysis of Platform Policies”, we outlined the data privacy and model training policies followed by major AI providers such as OpenAI, Google Gemini, Microsoft’s Azure OpenAI, and Anthropic’s Claude.

For those considering an on-premise solution, we recommend the blog post “Top Lightweight LLMs for Local Deployment” There, we reviewed several top open-source lightweight LLMs and explained how to run them on a local Windows machine—even with limited GPU resources.

Choosing the right AI tool for your company depends primarily on the goal it’s meant to achieve. A chatbot works best where quick and accessible customer service is key. An AI agent can automate repetitive internal processes and improve information flow between systems. An AI assistant provides day-to-day support for employees—offering suggestions, summaries, or preparing data for further use.

Large language models also allow integration with existing processes—without the need to build a dedicated tool from scratch. However, implementing AI-based technology requires a well-thought-out decision, taking into account both efficiency and data security. If you’re looking to adopt AI in your company and need an experienced partner to guide you through the process, get in touch with us.

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AI User Privacy: An Analysis of Platform Policies

Martyna Mul — Wed, 30 Apr 2025 08:35:35 +0000

Ever wondered where your data goes when you interact with AI cloud platforms? Or is it used to train future models? In this article, we’ll break down the data privacy policies of top AI platforms. You will also learn what to do to ensure your data is not used for training Large Language Models (LLM).

Major AI cloud providers have become increasingly transparent about their data usage policies – especially when it comes to training models. While most platforms, particularly those offering enterprise-level services, do not use your inputs and outputs for training by default, the fine print matters. Understanding how these services handle your data – and how you can maintain control – is essential.

In this article, we’ll break down the data privacy and model training policies of top AI platforms, including OpenAI, Google Gemini, Microsoft’s Azure OpenAI and Anthropic’s Claude. You’ll learn:

- How AI platforms use your data and whether your data is used to train models by default
- How to prevent AI from using your data opt, if needed
- Where your data is stored (data residency), and
- What compliance measures (like GDPR) apply

Adopting AI isn’t just about prompt engineering or model performance. It’s also about knowing where your data goes—and how to ensure it stays under your control.

Here’s what you need to know:

OpenAI – Data Usage and Privacy

OpenAI treats your data differently based on how you interact with its services:

ChatGPT App (Web/Mobile)

When you chat with ChatGPT, your conversations may be used to train AI models – unless you manually opt out. To prevent your data from being used:

- Go to Settings → Data Controls → Improve the model for everyone and toggle it off.
- Even with the opt-out, OpenAI stores chats for 30 days for abuse monitoring before deletion.

OpenAI API and ChatGPT Enterprise

If you’re a developer or a business using OpenAI’s API or ChatGPT Enterprise, there’s no need to opt out. By default, OpenAI does not use API or Enterprise data to train its models, and your data stays private. You don’t need to do anything to opt out – it’s already protected. You can choose to share data to help improve the model, but only if you want to.

Data Residency

OpenAI’s servers are mostly based in the United States, and currently, if you’re using the API directly, you can’t choose where your data is stored. That means your data is processed within OpenAI’s own infrastructure – protected by strong security, but not necessarily hosted in your country.

However, there’s some progress for enterprise users. OpenAI recently introduced an option for eligible enterprise API customers that allows data to be stored in Europe, provided there’s a specific agreement in place.

If regional data residency is important for your business – say, for GDPR or internal compliance – you might want to consider using Azure OpenAI, which hosts OpenAI’s models on Microsoft’s cloud. With Azure, you can choose a region like Western Europe or Asia, and all data processing and storage will stay within that geography.

We’ll dive into Azure more in the next section – but in short: OpenAI handles your data securely, but for strict control over where it lives, a partner cloud service like Azure may be a better fit.

Google (Gemini) – Google’s Approach to Your Data

Google’s foray into generative AI includes Gemini, a next-generation model that powers products like Google Gemini (the chatbot) and various enterprise AI offerings on Google Cloud. Here’s how they handle your data:

Gemini App

By default, Google does save your Gemini chat history to your account (much like search history) and may use it to improve their service. However, Google provides a “Gemini Activity” setting to control this.

To manage this:

- Visit Gemini Activity settings.

- Pause Gemini Activity to stop saving chats and prevent them from being used in AI model training data sources.

- You can also delete existing conversation history.

Turning off Gemini Activity means your new chats won’t be used to improve their machine learning services, nor will they be seen by human reviewers, unless you explicitly submit them as feedback. This gives regular users a way to opt out, similar to ChatGPT’s opt-out toggle.

To stop saving your conversations, go to the Activity tab and toggle Gemini Apps Activity. You can also delete your past conversations.

API and Vertex AI

If you’re using Google Cloud’s Vertex AI platform:

- Your prompts and outputs are not used to train AI models without explicit permission.

- Data may be cached briefly (up to 24 hours) for performance but remains within your selected geographic region.

- Businesses can opt for a zero-retention policy for maximum privacy.

Data residency

Data residency is a strong point for Google: you can choose which geographic region your AI service runs in (e.g. EU or US data centers), and Google will process and store data in that region to meet any data localization requirements.

Microsoft Azure OpenAI – Enterprise Data Protection by Design

Training Policy

Microsoft’s Azure OpenAI Service lets companies use OpenAI’s models through the trusted Azure cloud platform. Privacy is a major selling point here. Microsoft is very explicit: any data you send into Azure OpenAI is not used to train the underlying models or improve Microsoft’s or OpenAI’s services .

Microsoft’s Azure OpenAI Service essentially hosts OpenAI’s models (GPT-4, GPT-3.5, etc.) within the Microsoft Azure cloud. Microsoft has specifically designed this service for enterprises that require strong privacy protections. Key aspects are:

- Any data you input into Azure OpenAI – prompts, completions (model outputs), embeddings, fine-tuning data – is not used to train the AI models.

- Your inputs and outputs “are NOT available to other customers, are NOT available to OpenAI, and are NOT used to improve OpenAI models”.

- Microsoft only retains data as needed to provide the service and monitor for misuse. In fact, prompts and outputs on Azure are stored only temporarily (up to 30 days) by default, and solely for abuse detection purposes. After 30 days, those prompts are deleted. If even this temporary storage is a concern (say, for ultra-sensitive data), Microsoft offers a process called “modified abuse monitoring” where you can request that even the 30-day storage be bypassed, meaning no prompts are retained at all. Typically, you’d need approval for this exception, but it’s an option for high-security scenarios.

Data Residency

Because it’s on Azure, you also benefit from easily choosing the region and complying with data residency requirements. When setting up Azure OpenAI, you deploy the service to an Azure region (for example, East US, West Europe, Southeast Asia, etc.). All processing and data storage for inference will occur within that region or its geographical boundary. So, if you deploy in Western Europe, your data isn’t leaving Europe – crucial for GDPR compliance. Azure itself meets numerous compliance standards (SOC 2, ISO 27001, etc.), and these extend to Azure OpenAI as an Azure service.

Anthropic (Claude) – A Privacy-First AI Assistant

Training Policy

Anthropic, the company behind the Claude AI assistant (Claude 2 and newer versions), has emphasized a privacy-conscious approach from the outset. Anthropic adopts an opt-in approach:

- By default, Anthropic does not use your conversations or data to train its models. This applies to both their commercial offerings (Claude for Work, Anthropic API) and consumer products (Claude Free, Claude Pro) – your prompts and Claude’s responses aren’t automatically used for model training.

- They only use data if you deliberately opt-in, such as by providing explicit feedback. For instance, if you click a thumbs-up/down in a Claude interface or send data to their feedback channels, you’re essentially saying “you can learn from this”.

For enterprise clients, Anthropic offers Claude Team/Enterprise, which not only guarantees no training on your data but also provides admin controls. One such feature is custom data retention settings. By default, Anthropic’s systems might retain your inputs/outputs indefinitely for your account (though not for training). However, Claude Enterprise admins can set a retention policy – for example, you might set it to delete all conversation data after 30 days, 60 days, etc., with 30 days being the current minimum. These controls aim to support compliance with regulations like GDPR.

Data Residency

Anthropic is a newer player, and currently, when you use their API directly, you don’t explicitly choose a data region – it’s likely hosted in the US by Anthropic (or possibly through cloud providers like AWS in the US region). However, Anthropic models are also available through partners, which can help with data residency. For example, Anthropic’s Claude is offered via Amazon Bedrock (AWS’s AI service) and via Google Cloud Vertex AI. If you use Claude through one of these platforms, you can take advantage of AWS’s or Google’s region controls.

Conclusion

Understanding the data collection practices of LLM providers is crucial for AI compliance, customer trust, and corporate governance. Whether you’re focused on compliance, customer trust, or internal data governance, these insights help you make informed decisions. Choose providers that align with your privacy values – and always review your settings.

Here’s a comparison of major platforms:

Provider	Default Data Training	Web App Setting	Data Residency Options	GDPR/CCPA Compliance	Privacy Policy
OpenAI	No (API)	Opt-out available	No; (unless used via Azure Microsoft)	Yes	Consumer privacy
Google	No (Cloud + Gemini)	No training by default	Broad region control	Yes	Enterprise privacy, Gemini privacy, Vertex AI
Azure	No	N/A	Full regional control	Yes	Azure, OpenAI privacy
Anthropic	No	No training by default	No (unless used via partners)	Yes	API users, Claude.ai users

For maximum privacy and control, local deployment (on-premises models) is always an alternative. This avoids cloud storage concerns entirely. You can read more about local deployment here.

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Artykuł AI User Privacy: An Analysis of Platform Policies pochodzi z serwisu Inero Software - Software Consulting.

What are AI Agents and how can they help your company

Marta Kuprasz — Fri, 28 Feb 2025 09:51:15 +0000

The term artificial intelligence has been prominently featured in numerous publications as a solution to challenges related to efficiency, organization, and creativity. Many companies are following this trend, striving to incorporate AI-driven solutions into their offerings. These efforts take various forms. In this article, we will take a closer look at AI Agents, which can provide valuable support, particularly in back-office processes.

For some time now, we have been observing a significant rise in the popularity of terms related to the use of artificial intelligence. So, let’s start from the beginning.

What is "Artificial Intelligence"?

The term “artificial intelligence” encompasses Large Language Models (LLMs), natural language processing (NLP) systems, machine learning algorithms, neural networks, and generative AI models.

LLMs, such as ChatGPT from OpenAI or Gemini from Google, are models trained on vast datasets that can analyze, process, and generate text in a way that mimics human reasoning. They are used in various applications, ranging from chatbots and voice assistants to advanced systems supporting business analysis and process automation in companies.

Artificial intelligence is not limited to text processing. Modern models can also analyze images, audio, video, and numerical data, making them highly versatile tools in business. AI enables not only the automation of repetitive tasks but also the detection of patterns in large datasets, trend forecasting, and support for strategic decision-making in companies.

Who are AI agents?

“AI agents” are intelligent systems based on machine learning algorithms, natural language processing (NLP) models, and Large Language Models (LLMs). Their purpose is to automate processes, support decision-making, and interact with users in a natural and context-aware manner.

This means that virtual assistants are based on well-known and widely used LLMs such as ChatGPT, Gemini, Claude, Mistral, or DeepSeek, which can generate coherent responses, analyze texts, and adapt to the context of a conversation.

However, AI agents differ from language models in that they are designed to perform specific tasks autonomously. In practice, this means they are equipped with additional modules that enable them to gather information, process data in real-time, and make decisions based on business rules.

Unlike traditional chatbots, AI agents not only answer questions but can also handle complex processes, integrate with enterprise systems, and learn from user interactions. As a result, they are used in various areas, from administrative support and document analysis to the automation of operational processes in enterprises.

Also read: Meet Your Personal AI Agent – A Case Study for a Freight Company

The operation of AI agents is based on several key components:

- Communication interface – allows the agent to interact with users through text, speech, or other data formats.
- Decision engine – based on AI models and business rules, it enables situation analysis and the selection of optimal actions.
- Integration with external systems – AI agents often operate in conjunction with databases, business applications (ERP, CRM), or cloud services, allowing them to access up-to-date information.
- Process automation – they can perform specific tasks, such as generating reports, processing requests, sending notifications, or initiating predefined processes in IT systems.

What are the types of AI agents?

AI agents may take various forms depending on their application and level of autonomy. Leveraging advanced artificial intelligence models, they can assist users in a wide range of activities, from customer support to data analysis and business process management.

We can distinguish several main types of AI agents:

- Conversational agents – include chatbots and voicebots that interact with users through text or speech. They can answer questions, handle customer inquiries, and support sales processes.
- Analytical agents – specialize in processing and interpreting data. They use machine learning algorithms to analyze trends, detect anomalies, and generate reports.
- Operational agents – automate business tasks by integrating with enterprise systems. They can manage documentation, process documents, or coordinate activities within corporate processes.
- Autonomous agents – operate independently, making decisions based on collected data and predefined business rules. They are used in areas such as logistics, resource management, and dynamic operational planning.
- Decision-support agents – provide recommendations based on advanced data analysis, helping managers and specialists make strategic decisions.

Each of these types can operate independently or collaborate with other systems, creating a complex AI-driven environment. In the following sections, we will explore specific applications of AI agents and their impact on the operational efficiency of businesses.

Cloud or on-premise solution – how can an AI agent be implemented in a corporate environment?

Implementing an AI agent in an organization requires selecting the appropriate deployment model that best meets business, technical, and regulatory requirements. Companies can choose between a cloud-based solution (SaaS) or an on-premise deployment, depending on their needs for flexibility, security, and integration with existing systems.

The choice of the appropriate model depends on various factors, which are presented in the table below.

Comparison: SaaS vs On-Premise

Criterion	SaaS (Cloud)	On-Premise (Local)
Deployment model	Cloud-based (AWS, Azure, Google Cloud)	Operates on the company’s own infrastructure
Infrastructure	Cloud service provider	Local servers
Initial costs	Low	High
Operational costs	Subscription-based	Fixed maintenance and energy costs
Scalability	Very high	Limited (dependent on hardware)
Data security	Limited (processed outside the company)	High (full control over data)
Regulatory compliance	May require additional agreements and certifications	Easier to meet regulatory requirements
Ease of implementation	Easy and fast	Requires hardware purchase and setup
Updates and maintenance	Automatic, provided by the vendor	Self-managed updates and maintenance
Integration with enterprise systems	Strong API support and pre-built integrations	Full control but may require additional integration

The choice of the appropriate deployment model—cloud-based or on-premise—depends on the company’s specific requirements regarding security, costs, and integration with existing systems. Regardless of the chosen strategy, AI agents can significantly enhance operational efficiency and allow employees to focus on tasks that require creativity and strategic thinking.

The development of AI technology is undoubtedly one of the strongest technological trends in recent years. Therefore, it is worth considering now how AI agents can support your company’s growth and become a key element of its digital transformation.

We will create an AI Agent for your company.

Artykuł What are AI Agents and how can they help your company pochodzi z serwisu Inero Software - Software Consulting.

A year under the sign of artificial intelligence development

Marta Kuprasz — Mon, 18 Dec 2023 10:32:30 +0000

The end of the year is a time for summaries. In the world of IT, many interesting things have happened, so in this article, we decided to focus on AI. The development of artificial intelligence and its media presence accelerated to an unprecedented scale. Tools based on Large Language Models (LLMs) have been popularized and made widely available to users from various industries, not just technological ones. We decided to summarize the year with Andrzej Chybicki, the CEO of Inero Software. Here is the list he identified as the key 5 events of the past year.

Fact 1: OpenAI – artificial intelligence becomes widely accessible

OpenAI played a tremendous role in popularizing the field of artificial intelligence in the context of human language understanding. In 2022, they released ChatGPT, and in the following months, they presented new, improved models. These advancements not only improved the performance of existing applications but also opened new avenues for AI in healthcare, environmental science, administration, marketing, and more.

In 2023, ChatGPT saw remarkable advancements, featuring enhanced learning algorithms for improved accuracy and nuanced conversations, personalized user interactions, expanded language support for global accessibility, and broader application integration. OpenAI emphasized ethical considerations and bias reduction, incorporated real-time learning for up-to-date content, improved multimedia interaction capabilities, and boosted the tool’s robustness and reliability. Additionally, ChatGPT was tailored for specific industries, providing specialized functionalities and knowledge, marking a significant leap in AI technology and user-centric applications.

Expert Insight

OpenAI was the first widely recognized large language model. In the coming years, we are likely to see various versions of LLMs designed for specific applications – in fact, this has been happening for a few months now. OpenAI, despite being a pioneer, at least in terms of recognizability, is not always considered the best model for everything. The direction of development is certainly popularization in a similar way as it was with computers (i.e., LLMs like PCs) and specialization, meaning specialized language models designed for specific applications or even entities or people.

Fact 2: GitHub Copilot – a leader in AI/LLM implementation

One of the key roles in the development of artificial intelligence is played by Microsoft, which collaborates with OpenAI. Over the past year, Microsoft has continued to refine its vision of Microsoft Copilot. Let’s focus on the solution for developers: GitHub Copilot. In 2023 it underwent significant changes and enhancements. Here are the key updates:

In 2023, GitHub Copilot introduced several significant enhancements to bolster its role in AI-driven software development. The GitHub Copilot Chat, now generally available and powered by OpenAI’s GPT-4, provides more accurate code suggestions and explanations, using natural language to aid developers in various languages. This feature is integrated with both the GitHub platform and its mobile app, supporting coding, pull requests, and documentation. Additionally, GitHub Copilot Enterprise was introduced to tailor the tool to specific organizational needs, helping developers quickly adapt to their organization’s codebase and streamline tasks like documentation and pull request reviews, aimed at boosting enterprise-level productivity and security. The GitHub Copilot Partner Program was launched, integrating Copilot with various third-party developer tools and services, thereby creating a broad ecosystem that enhances the capabilities of developers using AI. Finally, GitHub unveiled new AI-powered security features in its Advanced Security suite, including a real-time vulnerability prevention system and application security testing features to detect and remediate code vulnerabilities and secrets, further securing the software development process.

Expert Insight

Thanks to its collaboration with OpenAI, Microsoft became a leader in AI/LLM implementation worldwide in 2023. Microsoft’s strategy in this area is based on using the LLM model to support (but not replace) as many activities and processes using Microsoft products as possible. Particularly important was ensuring an appropriate level of SLA (aligned with other Azure services) and data security. Among the most significant changes, apart from the mentioned GitHub Copilot (which aims to support developers in coding), are Copilot plugins available in practically all of this company’s flagship products (Word, Excel, PowerPoint, Outlook).

In December 2023, Microsoft also presented the CoPilot Studio solution, which enables the creation of low-code/no-code IT systems with significant support from the OpenAI model. This effectively allows for the easy expansion of existing Azure low-code solutions such as Azure Agents with conversational bots or AI-supported database adapters. Although CoPilot Studio is not yet available in its final form, Microsoft clearly communicates development directions and the advantages that developers, engineers, and users can experience from its use. From the presentations of Microsoft representatives, it can be inferred that Microsoft’s goal is to lower the entry threshold for creating and implementing new advanced AI solutions, as using low-code platforms does not require as deep technical knowledge as traditional coding. We can expect widespread interest in these solutions not only from the largest companies using MS Azure in the coming years. Currently, among experts, the question is not “whether to use AI” but how to implement it to not fall behind the competition. Those entities that create a coherent strategy for incorporating AI-based products into their processes in the coming years will be able to significantly benefit from the revolution that is already taking place.

Fact 3: The European AI Act: A Regulatory Milestone

On 14 June 2023, the European Parliament adopted its negotiating position on the AI Act. Parliament’s priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems. The AI Act sets different rules for different AI risk levels.

The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

Unacceptable risk

Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:

Cognitive behavioral manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behavior in children
Social scoring: classifying people based on behavior, socioeconomic status or personal characteristics
Real-time and remote biometric identification systems, such as facial recognition

Some exceptions may be allowed: For instance, “post” remote biometric identification systems where identification occurs after a significant delay will be allowed to prosecute serious crimes but only after court approval.

High risk

AI systems that negatively affect safety or fundamental rights will be considered high-risk and will be divided into two categories:

1) AI systems that are used in products falling under the EU’s product safety legislation. This includes toys, aviation, cars, medical devices and lifts.

2) AI systems falling into eight specific areas that will have to be registered in an EU database:

Biometric identification and categorisation of natural persons
Management and operation of critical infrastructure
Education and vocational training
Employment, worker management and access to self-employment
Access to and enjoyment of essential private services and public services and benefits
Law enforcement
Migration, asylum and border control management
Assistance in legal interpretation and application of the law.

All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle. For more information, visit the European Parliament website.

*source: https://www.europarl.europa.eu

Expert Insight

Ensuring security and confidentiality of data is certainly one of the most important issues concerning the implementation of AI solutions. Many experts indicate that despite the good intentions of the European Commission, the proposed solutions may contribute to reducing the competitiveness of the domestic AI market, which in effect will increase the distance between Europe and leaders in this field (i.e., the USA and China). I personally share these concerns. Here, a good example might be the similar situation that occurred about 15 years ago when cloud computing was being implemented. At that time, the EU also created a regulation governing the rules of access and data confidentiality (GDPR), which to this day is the regulatory basis in this area. At the same time, the largest solutions that most in the EU use are those developed in the USA, where the priority was the free development of technology, and only secondarily the legal framework. Unfortunately, many indications suggest that a similar situation might occur with AI.

Fact 4: Gemini: new model from Google

Without a doubt, the launch of Gemini was the most prominent premiere in the latter part of 2023, generating significant buzz. It is a result of large-scale collaborative efforts by teams across Google. It was built from the ground up to be multimodal, which means it can generalize and seamlessly understand, operate across, and combine different types of information including text, code, audio, image, and video.

Gemini 1.0 was trained to recognize and understand text, images, audio, and more at the same time, so it better understands nuanced information and can answer questions relating to complicated topics. This makes it especially good at explaining reasoning in complex subjects like math and physics.

During the presentation on the release of the Gemini API for developers, a lot of time was dedicated to AI Studio, a browser-based, free tool for code creation. The second focus was on Vertex AI, a more advanced program that allows for “both training and deploying ML (machine learning) models and AI applications.” Google offers the option to transfer a preliminary project developed in AI Studio to Vertex AI, to add additional features available within the larger platform of Google Cloud.

Expert Insight

Google has officially joined the large language model (LLM) race. The most intriguing aspect of what they propose is that their model will operate in three versions: Ultra (the most feature-rich), Pro, and Nano, with the latter being designed for mobile phones. It’s still unclear whether Nano will run entirely on client devices (smartphones) or if it will simply be a thin client and a kind of extension of Google Assistant. It’s also worth emphasizing that Google, like Microsoft, will offer Gemini services as elements of its flagship products, such as Google Sheets, Google Docs, and others.

Fact 5: Advancements in Natural Language Processing (NLP)

2023 witnessed remarkable progress in the field of Natural Language Processing. Researchers and companies globally made significant strides in improving the accuracy and versatility of NLP models. These advancements have led to more sophisticated understanding and the generation of human language by machines, paving the way for more intuitive and natural human-computer interactions. This year saw the deployment of advanced NLP in various applications, from customer service chatbots to complex data analysis tools, revolutionizing how we interact with technology daily. This progress in NLP technology not only enhanced existing applications but also opened new possibilities for AI in fields such as education, content creation, and multilingual communication.

Expert Insight

AI technologies are increasingly breaking the barrier of understanding natural language, gradually blurring the line between structured data previously used in IT systems and human knowledge. It seems that the creation of AGI (Artificial General Intelligence), a machine matching or even surpassing the average human in many aspects, is now just a matter of time. The challenge for the world of science, business, and politics will now be to direct the development of AI in a way that serves the broadly understood humanity and does not cause threats that many (probably rightly) fear.

The last 12 months have been rich in interesting AI releases. The presentation of new large language models has opened up a range of possibilities for their implementation in everyday tasks, both in programming work and creative teams. European authorities are trying to keep up with these changes and adapt legal regulations to be in line with the current technological situation. In the coming months, we will certainly see more premieres, as leading players like Google and Microsoft compete to create solutions that utilize artificial intelligence.

Artykuł A year under the sign of artificial intelligence development pochodzi z serwisu Inero Software - Software Consulting.

Document Data Processing using GPT model

Marceli Formela — Thu, 09 Feb 2023 14:49:42 +0000

GPT – A DIFFERENT POINT OF VIEW

Inero Software

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In the previous post, we looked at the possibilities of the GPT model in terms of supporting the work of a programmer. But that’s not the only thing this technology can do. GPT is a language model that utilizes a deep learning technique known as transformer. For example, this allows it to produce text that mimics human writing. It has been trained on an extensive collection of text found on the internet and can be adjusted for specific tasks such as language translation, text summarization and text generation. The model is multilingual and can respond to a broad spectrum of questions, commands and prompts. Additionally, it is capable of generating creative content like stories and poetry. It could be applied in various fields such as virtual assistants, chatbots and automated writing. However, it’s important to keep in mind that while it is highly advanced, GPT is not human and may not fully understand context of language nuances, thus it may produce errors or biases present in the training data.

We asked ChatGPT question: “Describe your ability to summarize texts.”

Output: “As a language model, I am able to summarize documents by identifying and extracting the main points and condensing them into a shorter version while retaining the key information. I can summarize documents of various formats such as text, PDFs, and articles. However, my ability to summarize a document may depend on the length and complexity of the document, as well as the quality of my fine-tuning. Additionally, my summary may not always be entirely accurate, as I am not capable of understanding the context or nuances in the same way a human would, but I can provide a summary which is more readable, informative and precise based on the data I have been trained on.”

Based on this information, we will conduct an experiment to test the efficiency of davinci-003 model during analyzing some documents that were prepared by us especially for this purpose. However, if we want the model to be able to effectively analyze the content of PDFs documents, we should provide it in the form of a prompt. For this we will use OCR technology and pyTesseract.

Inero Software

PREPARING THE DATA AND THE CODE

USING OCR

OCR stands for Optical Character Recognition, which is a technology used to convert scanned images or PDFs of text into machine-readable text. pyTesseract is a Python wrapper for Google’s Tesseract-OCR Engine, which allows for easy integration of OCR capabilities into Python scripts. pyTesseract can be used to extract text from images, scanned documents, or PDFs. It is an open-source library, and it can be easily installed via pip.

INTEGRATION WITH AN API

The next step is to use endpoint prepared for handling text completions. We can interact with the API through HTTP requests from many languages, for example via official Python bindings, Node.js library, or a community-maintained library. This time we will make use of the first one. We can add it to the project simply using following command:

pip install openai

All requests should include our API key in an Authorization HTTP header. A key should be retrieved from API Keys page and used in every request.

openai.api_key = os.getenv(“OPENAI_API_KEY”)

In case of summarizing input text we can use endpoint that creates a completion for the provided prompt and parameters.

POST https://api.openai.com/v1/completion

model is the specific pre-trained language model that the OpenAI API will use to generate text.
prompt is the input text that the API will use as a starting point for generating new text. This can be a sentence or a paragraph, and is used to provide context for the text generation.
max_tokens is an integer value that specifies the maximum number of tokens (words or word pieces) that the API will generate in its response.
temperature is a value that controls the “creativity” of the generated text. Lower values will produce text that is more conservative and similar to the input prompt, while higher values will produce text that is more varied and creative.

DATA PREPARATION

Now we will use some Carrier’s Liability Insurance certificates containing fake data.

Carrier’s liability insurance is a type of insurance coverage that protects a carrier, such as a shipping company, against claims made by the customer for loss or damage to the goods being transported. It provides financial protection to the carrier in the event that goods are damaged or lost while in transit.

Here we have the structure of these documents:

For the purposes of this experiment, we have prepared 6 variants of such a document, which are available for download HERE. They differ, for example, in the insured, the insurance period, the limit of liability, the insurer, and the territorial scope.

For each of the variants, 7 questions were asked to check how well the model deals with understanding the content.

Who is the insured? Give me name and NIP number.
Who is the insurer?
What is the limit in general liability?
What is the certificate number?
What is the scope of international transport?
Where this documents was issued?
What is the insurance period?

Below are the answers:

OCP-01
The insured is XYZ S.A. with NIP: 000-00-00-001. The insurer is Towarzystwo Ubezpieczen ABC S.A. The limit in general liability is 300.000,00 EUR per occurrence. The certificate number is 123456789. The scope of international transport is Europe including the countries of the former USSR, such as Russia, Belarus, Ukraine and Moldova, and the Asian part of Turkey. This document was issued in Warsaw, Poland and the insurance period is from 01.01.2023 to 01.01.2024.

OCP-02
The insured is ZYX S.A. with NIP: 000-00-00-002. The insurer is Towarzystwo Ubezpieczen BCA S.A. The limit in general liability is 150.000,00 EUR per occurrence. The certificate number is 234567891. The scope of international transport is Europe including the countries of the former USSR, such as Russia, Belarus, Ukraine and Moldova, and the Asian part of Turkey. This document was issued in Warsaw, Poland and the insurance period is from 01.01.2024 to 01.01.2025.

OCP-03
The insured is YXZ S.A. with NIP: 000-00-00-003. The insurer is Towarzystwo Ubezpieczen CBA S.A. The limit in general liability is 420.000,00 EUR per occurrence. The certificate number is 345678912. The scope of international transport is Europe including the countries of the former USSR, such as Russia, Belarus, Ukraine and Moldova, and the Asian part of Turkey. This document was issued in Warsaw, Poland and the insurance period is from 01.06.2023 to 01.06.2024.

OCP-04
The insured is CCC S.A. with NIP: 000-00-00-004. The insurer is Towarzystwo Ubezpieczen VCX S.A. The limit in general liability is 500.000,00 EUR per occurrence. The certificate number is 456789123. The scope of international transport is Europe including the countries of the former USSR, such as Russia, Belarus, Ukraine and Moldova, and the Asian part of Turkey. This document was issued in Warsaw, Poland and the insurance period is from 01.02.2023 to 01.02.2024.

OCP-05
The insured is CCTA S.A. with NIP: 000-00-00-005. The insurer is Towarzystwo Ubezpieczen VVV S.A. The limit in general liability is 440.000,00 EUR per occurrence. The certificate number is 678912345. The scope of international transport is countries of the European Union (excluding Spain and Portugal), including Germany. This document was issued in Warsaw, Poland and the insurance period is from 02.04.2023 to 02.04.2024.

OCP-06
The insured is AVX S.A. with NIP: 000-00-00-005. The insurer is Towarzystwo Ubezpieczen HFX S.A. The limit in general liability is 750.000,00 EUR per occurrence. The certificate number is 567891234. The scope of international transport is countries of the European Union (excluding Romania, Bulgaria, Poland), including Germany. This document was issued in Warsaw, Poland and the insurance period is from 02.07.2023 to 02.07.2024.

Inero Software

SUMMARY

The model correctly interpreted the data contained in the documents, despite some potential issues related to OCR tool analyzing multi-column text. The model totally dealt with the text in Polish and correctly processed the information contained therein, despite the question written in English. Requests were processed separately, per document, but still consistency in text style was maintained. We can assume that in the near future such solutions will become helpful in broadly understood information processing.

Artykuł Document Data Processing using GPT model pochodzi z serwisu Inero Software - Software Consulting.

ChatGPT – A developer’s point of view

Marceli Formela — Wed, 18 Jan 2023 10:28:43 +0000

INTRODUCTION

ChatGPT was introduced a few weeks ago and since then it has been one of the main topics in the IT industry. In this blog post, we’ll take a closer look at what GPT is and the potential applications of this powerful technology in the hands of software engineer. GPT-3 is the third and newest generation of GPT models and it is one of the most powerful language so far. It is trained with a big amount of text data from multiple sources which include websites and books. It can be still fine-tuned for a wide range of tasks, including text summarization, extracting data from documents and question answering in one of the many specific domains.

ChatGPT, on the other hand, is the most recent specialized version of the GPT-3, tailored to the needs of chatbot applications. Reinforcement Learning with Human Feedback (RLHF) was used to improve the model’s outputs by incorporating human feedback during training. By learning from human corrections and preferences, the model’s output can become more accurate. In many cases this model can be generally faster and more efficient than GPT-3 and that’s why it can be a better choice for use in real-time use-cases. However, in general it may be not as powerful as the source model which can be additionally fine-tuned. They both seems to be designed for different purposes.

There are also other GPT-3-based (older) models that are specifically designed to work with code rather than general natural language. OpenAI Codex is pre-trained on a large dataset of code, which allows it to ‘understand’ the structure and semantics of code. One of the most popular Codex-based tools is Github Copilot that uses this model to suggest code and entire functions in real-time even from IDE.

That’s some theory. But can we efficiently work with code, at least in some way, using the newest solution – ChatGPT instead of Codex-based ones?

PLAYING IN THE PLAYGROUND

#CASE 1

In the first step, we checked how the model deals with modelling domain classes and incorporating the Domain Driven Design (DDD) approach (at least in very basic version). In short, Domain-Driven Design (DDD) is methodology that aims to bridge the gap between business and software by aligning the structure of the code with the business domain it represents. DDD utilizes principles and patterns to create a clear mapping between the business and software, making it easier for developers to understand the code and for domain experts to understand the software.

Input: Give me an example of using Domain Driven Design. Let this be an example of a car booking system. There we should have reservations, users and cars. Indicate what could be Value Object, Entity, and Aggregate.
Output:

ChatGPT correctly generated classes for the domain objects we specified, assigned them some useful properties and methods, and correctly marked value objects, aggregate roots and entities. No errors, no code smells.

#CASE 2

Input: Write a C# method with a string parameter that returns a Base64 version of the input GUID.
Output:

All lines of code seem to be at least correct again. But it’s important to handle exceptions in any method that deals with input that can be malformed or otherwise invalid. In fact, there is a place where exception could be thrown. All we have to do is just ask for changes.

Input: What about handling exceptions?
Output:

#CASE 3

Input: Prepare a simple .NET API which returns current weather in a specified city.
Output:

GET method asynchronously calls an external API – OpenWeatherMap to retrieve temperature and description. It also does understand that we need to generate our own API key. In the next lines there is a mapping from JSON to Weather class. It does not use any mapping library nor does it manually, instead it makes some workaround using JsonSerializer. It seems like a fully acceptable solution.

SUMMARY

In many cases, picking the right model is a matter of preference. It seems like Codex/Copilot is more responsive, while ChatGPT is more proactive at work. Copilot can be a good choice for preparing short snippets or scanning all your code, and everyone else’s, and coming up with little adjustments on the fly. It learns style conventions and will adapt to suggestions. It really speeds up the amount of characters on the page and makes refactoring easier as well.

But ChatGPT is good to get broad architectural outlines and answer our questions. It’s easier to use this model for inspiration / exploratory work because we can specify what parts we don’t like and how we would like to change them. It’s more like an AI assistant where you can enter a piece of code and ask it to optimize or refactor it.

Both solutions work in different way but in many cases provide optimal answers if we ‘ask’ them in a proper way. Codex-based tools work more like a tactical advisor, making our tasks done faster and in a more effective way, while ChatGPT can possibly provide us improvements and alternative methods. From a practical point of view they both still are great choice for one-off utilities and functions.

What we have presented here is only a teaser, there is much more to explore. Technologies like these can help software engineers to be more productive by automating tasks that are typically time-consuming or repetitive, such as writing code, documentation and debugging, which allows programmers to focus on more complex and creative tasks.

Overall, GPT-like models can be a valuable tool for programmers when used in conjunction with other programming tools and best practices, but it’s not a complete replacement for a human software engineer. Sometimes they can be wrong and we have to keep that in mind.

In Inero Software we build digital products for digital logistics of tomorrow. We connect active aproach to analysis of business needs and combine with cutting edge technologies and AI/ML.

Artykuł ChatGPT – A developer’s point of view pochodzi z serwisu Inero Software - Software Consulting.