What it is
Retrieval-Augmented Fine-Tuning (RAFT) is a model training method that teaches a model how to use retrieved documents when answering domain-specific questions — including how to ignore irrelevant or distracting documents. Unlike RAG alone (which retrieves at inference time) or fine-tuning alone (which bakes knowledge into weights), RAFT trains the model to reason critically about retrieved context. It is aimed at enterprise uses where accuracy on a company's own internal content matters most.
Why it matters
Standard RAG can struggle when retrieved documents are noisy or only partly relevant; RAFT trains the model to handle that. For Salesforce customers in regulated industries like financial services or healthcare, a RAFT-tuned model can reduce wrong answers on internal policy and product documentation. It is a step beyond prompt engineering alone, and it costs more effort to set up.
Key components
- domain document corpus
- distractor document training
- chain-of-thought supervision
- fine-tuning pipeline
- retrieval integration
How it connects
RAFT training happens outside Salesforce. The resulting model can be brought into Salesforce through Einstein Studio's bring-your-own-model options, with the Trust Layer governing its outputs.
Good to know
RAFT requires labeled training data that includes both relevant and distractor documents — budget for dataset curation before assuming a quick win.
Related terms
BYOK (Bring Your Own Key)
A model where users provide their own API keys for AI services (like OpenAI, Anthropic, or other LLM providers) instead of relying on the platform's bundled AI.
Foundation Model
A large AI model trained on broad data at massive scale that serves as a general-purpose substrate other applications fine-tune or prompt against. The category that includes GPT, Claude, Gemini, Grok, Llama, and the rest of the frontier model lineup.
Grounding
Connecting AI responses to your actual business data so agents answer with facts, not hallucinations.
RAG (Retrieval-Augmented Generation)
A technique that makes AI smarter by fetching relevant information from your data before generating a response. The AI "looks it up" instead of guessing.
Vendor-Neutral AI
An architecture pattern where AI capabilities — skills, agents, evaluations — are defined separately from the LLM vendor that runs them, so the same capability can execute on Anthropic, OpenAI, xAI, Gemini, or local models without rewriting.
Retrieval-Augmented Fine-Tuning in practice
Articles from the Funnelists Team that put this term to work.
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