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Private RAG and Internal Knowledge Search Guide
Retrieval-Augmented Generation is useful when employees need answers grounded in approved internal sources. The retrieval system, permissions and source quality usually matter more than a dramatic model demonstration.
Planning context
Why this decision deserves structure
Retrieval-Augmented Generation is useful when employees need answers grounded in approved internal sources. The retrieval system, permissions and source quality usually matter more than a dramatic model demonstration.
Guide section
What RAG does
Documents are parsed and indexed. When a user asks a question, the system retrieves relevant passages and supplies them to a language model. The interface can return an answer with citations so the user can verify the source.
Guide section
What RAG does not solve automatically
RAG does not guarantee accuracy, fix outdated policies, infer missing permissions or determine which document is authoritative. Retrieval can still miss relevant evidence or return misleading context.
Guide section
Designing the source pipeline
Inventory repositories, ownership, update frequency, deletion and document structure. Chunking, metadata and ranking are tested against representative questions instead of selected only by convention.
Guide section
Preserving permissions
The index and result interface must filter by the user’s approved access. Matter, client, department and document-level boundaries may require different connector and identity designs.
Guide section
Evaluating before production
Create questions with expected sources, difficult wording, conflicting documents and no-answer cases. Measure retrieval, citation and answer usefulness separately, then monitor failures after launch.
Use before procurement
Working checklist
- Authoritative source register
- Update and deletion synchronization
- Permission-aware retrieval
- Citations and source access
- Representative evaluation set
- No-answer and escalation behaviour
- Monitoring and feedback ownership
FAQ
Questions about this topic
Does RAG train the model on our documents?
Typically no. Relevant passages are retrieved at question time, though exact data flows depend on the selected services.
Can it run on-premise?
Yes, subject to model, storage, concurrency and support requirements. Canadian-hosted and hybrid designs are also possible.
How often is the index updated?
The schedule follows how frequently sources change and which connector capabilities are available.
Implementation support
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