RAFT: Retrieval-Augmented Fine-Tuning
Master RAFT methodology for adapting language models to domain-specific knowledge through retrieval-augmented fine-tuning
45 min read•Advanced
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What is RAFT (Retrieval-Augmented Fine-Tuning)?
RAFT is a fine-tuning methodology that trains language models to answer questions by retrieving and reasoning over domain-specific documents. Unlike standard fine-tuning, RAFT specifically teaches models to identify relevant information from retrieved documents and ignore irrelevant "distractor" documents, creating more robust domain-adapted models that can effectively use retrieval at inference time.
RAFT Training Calculator
Dataset Composition
Total Questions:50,000
Oracle Examples:25,000
Distractor Examples:25,000
Chain-of-Thought:15,000
Training Hours:5.2
Expected Accuracy:95%
Retrieval Efficiency:100%
Estimated Cost:$21
Implementation Examples
RAFT Dataset Generator
Automated RAFT Dataset Creation
RAFT Training Pipeline
Complete RAFT Training System
Production Inference System
RAFT Model Inference Pipeline
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