#rag

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Fine-tuning — a scalpel, not a universal hammer

Fine-tuning changes model weights. It is powerful when you have data, evals and a clear reason. It is an expensive mistake when it hides a bad prompt, missing RAG or an unclear process.

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Golden Dataset — ground truth for an AI system, not a golden cage

A Golden Dataset is a small, carefully reviewed set of real cases used to tell whether an AI system actually works. Using a course recommender as a worked example: it helps evaluate recommendations against real content, not marketing blurbs.

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Context window — how much hell fits in a prompt

A context window is how many tokens a model can see at once. A bigger window is not memory, truth or a guarantee of better answers. It is a larger, pricier workbench.

Library

RAG — Retrieval-Augmented Generation

When the model does not have your data in its head, it has to fetch it. RAG is a pattern, not a product: build retrieval first, generation second.