Rag - Tag - vo.rs

Fine-Tuning vs Prompting vs RAG: Picking the Right Tool Without Wasting GPU Hours

Tue, 09 Jun 2026 12:00:00 +0000

When a language model is not behaving as you would like, there is a powerful temptation to reach straight for the heaviest tool in the shed. People hear “fine-tuning,” picture a model retrained on their data, and book a pile of expensive GPU hours before they have even worked out what the actual problem is. More often than not, the result is wasted money and a model that is no better. The truth is that prompting, retrieval, and fine-tuning solve genuinely different problems, and choosing well saves you both effort and grief. This guide gives you a clear framework for picking the right one.

Talking to Your Documents: A Practical RAG Pipeline with Open-Source Tools

Wed, 27 May 2026 09:00:00 +0000

There is a particular kind of frustration in knowing that the answer you need is somewhere in a forty-page PDF, and that finding it means reading all forty pages. Retrieval-Augmented Generation turns that pile of documents into something you can simply talk to. Ask a question in plain English, and the system finds the relevant passages and answers from them. The very best part is that you can build a working version yourself, on your own machine, using only open-source tools and a modest Python script. This guide walks through exactly that — a small but complete RAG pipeline that lets you interrogate your own documents.

RAG Explained: How AI Stops Making Things Up

Tue, 07 Apr 2026 11:30:00 +0000

Imagine a brilliant colleague who has read most of the internet, speaks with unshakeable confidence, and occasionally invents a fact so smoothly that you only catch it because you happen to know the truth. That is a large language model on a bad day. It is not lying, exactly; it simply does not know what it does not know. Retrieval-Augmented Generation, or RAG, is the technique that hands that colleague a library card and a quiet instruction: before you answer, go and look it up. The result is an AI that grounds its words in real documents rather than in the foggy recollections of its training data.