Knowledge hub
RAG Explained
RAG connects a language model to a separate knowledge base and supplies relevant information as context for an answer.
Define the starting point and goal
RAG connects a language model to a separate knowledge base and supplies relevant information as context for an answer. In practice, this point should be linked to a clear owner, a reviewable initial corpus and a shared expectation for outcomes.
For implementation, sources, access and tests are not checked off once; they are handled as an ongoing maintenance responsibility.
Select suitable sources
RAG connects a language model to a separate knowledge base and supplies relevant information as context for an answer. In practice, this point should be linked to a clear owner, a reviewable initial corpus and a shared expectation for outcomes.
For implementation, sources, access and tests are not checked off once; they are handled as an ongoing maintenance responsibility.
Clarify roles, permissions and maintenance
RAG connects a language model to a separate knowledge base and supplies relevant information as context for an answer. In practice, this point should be linked to a clear owner, a reviewable initial corpus and a shared expectation for outcomes.
For implementation, sources, access and tests are not checked off once; they are handled as an ongoing maintenance responsibility.
Start with reviewable questions
RAG connects a language model to a separate knowledge base and supplies relevant information as context for an answer. In practice, this point should be linked to a clear owner, a reviewable initial corpus and a shared expectation for outcomes.
For implementation, sources, access and tests are not checked off once; they are handled as an ongoing maintenance responsibility.
| Goal | A clearly scoped work benefit rather than a general AI initiative. |
|---|---|
| Foundation | Current, suitable and authorised content rather than unchecked data volumes. |
| Control | Source references, responsible review and documented ownership. |
Frequently asked questions
Yes. A clearly bounded knowledge area makes review, adoption and learning easier.
No. Answers can be incomplete or unsuitable and require a defined review framework.
Sources make statements more traceable. They do not replace assessment of currency and context.
Sources and context
- NIST: Retrieval-augmented generation
- BSI: Artificial intelligence
- European Commission: Data protection
Note: These sources provide context. They do not replace legal or technical review of a specific use case.