# RAFT

> Train the model to be robust to irrelevant retrieved documents (distractors) in a domain-specific RAG setting.

- **Category**: Agentic AI
- **Subcategory**: Retrieval & RAG
- **Canonical URL**: https://designpattern.fyi/patterns/raft/

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## Description
**Intent**: Train the model to be robust to irrelevant retrieved documents (distractors) in a domain-specific RAG setting.
**Context**: A team is using retrieval-augmented generation in a specific domain and has observed that retrieval almost always returns a mix of documents. Some of the retrieved chunks are genuinely relevant to the user's query; others are topically similar distractors that share keywords or themes but do not actually answer the question. An off-the-shelf retrieval-augmented model attends to all of these chunks and is over-confident on the distractors that look plausible at a glance.
**Solution**: Construct training examples where some documents are oracle and others are distractors. Train the model to cite oracle documents and ignore distractors. Couples chain-of-thought with citation discipline.


## Use Cases
- Domain-specific RAG models drift to topically similar distractors.
- Training data with oracle and distractor documents can be constructed at scale.
- Citation discipline matters and outputs must be traceable to oracle sources.





## Trade-offs


### Advantages

- Robustness to distractor documents in domain RAG.

- Citation discipline improves.




### Considerations & Drawbacks

- Training data effort.

- Domain-specific; transfer between domains is partial.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/raft/)