Retrieval-Augmented Fine-Tuning (RAFT)
A training technique that combines retrieval-augmented generation with fine-tuning, teaching models to better leverage retrieved context. Produces models that are both knowledgeable and grounded in source documents.
Why it matters
RAG lets a model pull in outside documents at answer time, but models aren't always good at actually using what's retrieved, and sometimes ignore it. RAFT trains the model specifically to read retrieved passages well and lean on them instead of guessing. For teams building AI over their own knowledge base, this means more answers grounded in the real documents and fewer confident-sounding mistakes.
In practice
A company builds an internal assistant over its policy manuals. With plain RAG, the model sometimes retrieves the right page but still answers from memory. After RAFT, it's been trained on examples where the correct answer comes straight from the retrieved text, and also to ignore irrelevant passages. Now when an employee asks about a leave policy, the reply reliably reflects what the actual manual says.
Related terms
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Attention Mechanism
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Distillation
A technique where a smaller 'student' model learns to replicate the behavior of a larger 'teacher' model. Produces compact models that retain most of the teacher's capability while being faster, cheaper, and deployable on smaller devices.