In this post 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.
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).
In this post, we’ll explore several top open-source lightweight LLMs and how to run them on a local Windows PC—whether CPU-only or with a limited GPU—for document processing tasks.
Whether you’re building a custom chatbot, agent, an AI-powered code assistant, or using AI to analyse documents offline, local deployment empowers you to experiment and innovate without relying on external services.
In this post, we conduct a comparative analysis of three popular LLMs—OpenAI’s GPT based models: 4o-mini and o3-mini, and open-source DeepSeek R1—to evaluate their effectiveness in reading and analyzing statistical data from large PDFs.
This post explores how DeepEval helps systematically assess the effectiveness of both retrieval and generation components, ensuring more reliable machine-generated insights.
In this post, we’ll explore how to implement object detection directly in the browser using YOLO
