Source ownership
Identify who owns each source, who may access it, and what content should never enter the retrieval corpus.
Retrieval & knowledge systems
Ground AI in controlled, traceable knowledge
Retrieval-augmented generation (RAG) and search-backed assistants are not only about embeddings or chunking. A reliable knowledge system needs source ownership, permissions, freshness, deletion, retrieval evaluation, evidence visibility and safe no-answer behavior.
Ingestion boundaries
Define supported file/API sources, normalization, document identity, duplicate handling and failure behavior during ingestion.
Retrieval quality
Choose retrieval and ranking behavior based on the task, then test with representative queries rather than relying on one demo.
Access filtering
Enforce user/document permissions before content reaches generation. Do not rely on the model to hide material it should not have received.
Freshness and deletion
Know how updates, removals and retention changes propagate so stale or deleted knowledge does not remain silently available.
Source traceability
Where appropriate, expose the sources used for an answer so users can inspect evidence and distinguish retrieval-backed content from generation.
No-answer behavior
Allow the system to say that evidence is missing, conflicting or insufficient instead of forcing a confident answer.
Evaluation
Test retrieval misses, access-boundary cases, outdated sources, ambiguous queries and answer faithfulness using real task examples.
Let’s work together
Building a knowledge-backed assistant?
Share the source types, access model, update frequency, user questions and what the system should do when trustworthy evidence is unavailable.