RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) explained
The approach is useful when answers should draw on particular documents or data. Sources may be internal or public. This makes them neither automatically current nor correct; both depend on maintenance and selection.
Implementation involves several tasks: organise documents, find suitable passages, enforce access rights and connect answers to the retrieved material. Vector search is one possible retrieval method. Keyword search or a combination of methods may also be relevant.
Errors can occur at each stage. Search may find the wrong product version; an important limitation may be missing from an excerpt; the model may draw an unsupported conclusion. A source link alone does not prevent these errors. Evaluate retrieval quality separately from answer faithfulness.
Operation requires clear ownership: who maintains the documents, how do updates enter the system and what happens when no suitable source is found? A transparent clarification or handover to a specialist may be more useful than an apparently complete answer.
Examples
Hypothetical application
An internal assistant answers questions about approved product documentation. A request retrieves conflicting versions. Instead of mixing values, the draft identifies the conflict and refers to the responsible specialist. The team deliberately includes such cases in testing.
Key Points
- Evaluate retrieval and generation separately.
- Maintain sources and enforce appropriate access.
- Treat missing evidence as an explicit case.
Practical application
Test representative questions against approved documents and distinguish retrieval failures from answer failures.
Useful measures
Relevant retrieval
Does retrieval supply the information needed for a test question?
Source faithfulness
Are claims supported by the sources actually supplied?
Common mistakes
- Assuming every retrieved source is current and correct.
- Applying permissions only after retrieval.
Sources and context
- Lewis et al.: Retrieval-Augmented Generation
Research on combining retrieval with text generation.
Frequently Asked Questions about RAG (Retrieval-Augmented Generation)
No. Unsuitable retrieved material and unsupported conclusions remain possible. RAG provides a way to incorporate and check sources.
No. RAG supplies retrieved content; fine-tuning changes model parameters through additional training. Applications can combine them.
No. Retrieval design depends on data, search tasks and existing systems.
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