RAFT (Retrieval-Augmented Fine-Tuning)

Definition

A training technique that combines retrieval-augmented generation with supervised fine-tuning by teaching a model to answer questions given both relevant and irrelevant retrieved documents. RAFT trains the model to cite the right sources and ignore distractors, producing more accurate and grounded responses than either RAG or fine-tuning alone.

Why it matters

Standard RAG retrieves context but the model may ignore it or hallucinate despite it. Standard fine-tuning bakes in knowledge but it goes stale the moment your documents change. RAFT bridges the gap — it produces a model that knows how to use retrieved information effectively, distinguishing signal from noise in the retrieval results. This is the next evolution of enterprise AI deployment for anyone whose accuracy requirements exceed what vanilla RAG delivers. If your RAG pipeline is retrieving the right documents but the model still gets answers wrong, RAFT is the architectural fix, not more prompt engineering.

In practice

UC Berkeley, Microsoft, and Meta published the RAFT paper in March 2024, demonstrating a 35% accuracy improvement over base RAG on domain-specific benchmarks like HotpotQA and HuggingFace documentation. The Gorilla LLM team at Berkeley led the research, showing that training with a mix of relevant ('oracle') and irrelevant ('distractor') documents teaches the model robust source discrimination. Early enterprise adopters are applying RAFT for legal document analysis, medical literature review, and financial compliance — domains where citing the correct source is not optional. The technique works with any fine-tunable model and pairs naturally with existing RAG infrastructure.