Lewis et al. (2020), Retrieval-Augmented Generation
Original RAG research paper; BGPD Labs adds governance and review as an implementation interpretation.
Learn more: Lewis et al. (2020), Retrieval-Augmented GenerationKnowledge systems · Applied AI
Why RAG needs governed evidence, not just vector search
Retrieving similar text does not automatically produce a trustworthy answer.
Three different systems
A chatbot generates conversation, semantic search finds similar material and governed retrieval-augmented generation (RAG) constrains responses to a defined corpus with evidence and review. These are related but not interchangeable.
Govern the corpus
Authority, scope, metadata, version, freshness and approval state matter before chunking or retrieval is tuned. Conflicting sources need an explicit handling rule, not a silent ranking assumption.
Grounding and escalation
Useful answers identify supporting sources, distinguish evidence from interpretation and abstain when the corpus cannot support a conclusion. Domain review and a sandbox prevent unreviewed material from entering the approved corpus.
Applied lesson
FIDES explores discovery, AI-assisted classification, validation, sandbox review, an approved corpus and Spanish retrieval with citations. It does not claim doctrinal authority or eliminate hallucinations.
Evaluation
- Test retrieval and citation quality with representative questions
- Track stale, conflicting and missing sources
- Define guardrails and escalation paths
- Keep production inclusion separate from discovery
Where it applies
The same discipline helps with regulated, technical, procedural and institutional knowledge. Evidence governance is a continuing operating practice, not a one-time model choice.