TL;DR
An advanced machine learning training methodology that teaches language models how to ignore irrelevant distractor documents and extract accurate answers from retrieved context.
Retrieval-Augmented Fine-Tuning bridges the gap between Retrieval-Augmented Generation and traditional fine-tuning. Unlike standard domain adaptation which trains on raw datasets, RAFT prepares a model by exposing it to questions alongside both context-relevant oracle documents and irrelevant distractors. By training the model to prioritize oracle documents and ignore distractors, it enhances the model's in-context reasoning and reduces reliance on perfect retrieval systems.
Why this matters for your business
It enables organizations to build highly accurate, domain-specific AI assistants that excel in private workflows like legal analysis or medical triage. This reduces hallucinations on private proprietary data without requiring massive parameter modifications.
