What is RAG?
Retrieval-augmented generation — the technique that makes a model answer from your documents instead of its training data.
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What is retrieval-augmented generation?
Retrieval-augmented generation, or RAG, searches your own documents for passages relevant to a question and gives them to a language model as context before it answers. This grounds responses in your actual content, allows every answer to cite its source, and lets the system refuse when the information is not present.
Why it matters more than the model
If the right passage never reaches the model, no amount of prompt engineering produces a correct answer. Retrieval quality — how documents are split, how they are indexed, how results are ranked — determines whether the system is trusted or abandoned.
Almost every disappointing RAG deployment we are asked to fix fails at retrieval rather than generation.
RAG or fine-tuning?
Retrieval for facts, fine-tuning for behaviour. If the model needs to know your current pricing, use retrieval — a fine-tune bakes in a snapshot that goes stale. If it needs to write in your house style or classify into your categories, fine-tune.
Choosing wrongly between them is the most expensive mistake in this category.
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