RAFT: Retrieval-Augmented Fine-Tuning
Free while signed in. Answers cite the passages they came from.
RAFT is a fine-tuning recipe that teaches LLMs to handle distractor documents during RAG and to answer with CoT-style citations to retrieved passages.
Distractor-aware training: Each training example mixes relevant documents with distractors, forcing the model to learn to ignore irrelevant retrieved content rather than averaging over it.
CoT + citations: Responses are trained to walk through chain-of-thought reasoning while verbatim-quoting the supporting passages, improving both accuracy and transparency.
Domain-specific RAG: Evaluated on PubMed, HotpotQA, and Gorilla (API-calling), RAFT consistently improves open-book in-domain QA over both plain fine-tuning and plain RAG baselines.
Practical recipe: Positions RAFT as a post-training step that upgrades a pretrained LLM for production-grade RAG without needing a new architecture.
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