RAG Workflows

llm.port supports Retrieval-Augmented Generation (RAG) for document-grounded responses.

What RAG enables

• Ingest enterprise documents
• Search relevant context at query time
• Improve answer quality with controlled knowledge sources

Typical lifecycle

1. Upload and organize source documents
2. Publish or activate knowledge for use
3. Query through the Gateway or chat experiences
4. Monitor usage and quality outcomes

Public deployment guidance

• Start with a focused knowledge scope
• Define content ownership and refresh cadence
• Validate permission boundaries before broader rollout

Notes

The public docs describe RAG at capability level. Deep indexing and processing internals remain internal.

Screenshots