Real-world lessons from research, reading, and applied experiments in AI.
In a world where AI models can process millions of tokens in a single context window, does Retrieval-Augmented Generation (RAG) still matter? Yes — and here’s why it’s more essential than ever.
Learn what context windows are, why they matter in Large Language Models, and how they affect tasks like chatbots, document analysis, and RAG pipelines.
A beginner-friendly introduction to Retrieval-Augmented Generation (RAG) and why it matters in the world of AI.
A clear, intuitive explanation of how LLMs like GPT-4 and GPT-5 actually work under the hood — with a special focus on the attention mechanism that lets them understand context.
A complete primer for developers moving from SaaS APIs like OpenAI to running open-source LLMs locally and in the cloud. Learn what models your MacBook can handle, how to size for RAG pipelines, and how GPU servers change the economics.
Understand the five essential components of a Retrieval-Augmented Generation (RAG) pipeline and how they work together to make AI smarter, faster, and more reliable.
Understand the five essential components of a Retrieval-Augmented Generation (RAG) pipeline and how they work together to make AI smarter, faster, and more reliable.
Understand the key differences between Retrieval-Augmented Generation (RAG) and fine-tuning, and learn which approach is right for your AI project.