Enterprise RAG Readiness Scorecard: Is Your Knowledge Base Ready for AI?
A practical scorecard for evaluating source quality, permissions, freshness, retrieval design, citations and operational readiness before deploying enterprise RAG.
RAG quality starts before embeddings
Many retrieval-augmented generation projects begin with a vector database and only later discover that the source material is inconsistent, duplicated, outdated or inaccessible. Retrieval cannot manufacture trustworthy knowledge from an ungoverned corpus. A readiness scorecard forces the organisation to evaluate the knowledge system before treating it as AI infrastructure.
The core question is not “Can we embed these documents?” It is “Can a user trust an answer derived from these documents, and can we prove why?” That requires ownership, access control, lifecycle rules and evidence at the source layer.
Score source authority and ownership
Every corpus should identify which sources are authoritative for which topics. A signed policy may override a wiki page; a current product specification may supersede an old implementation note. Assign source owners, review dates, sensitivity labels and supersession relationships. If two documents conflict, the retrieval layer needs a deterministic way to prefer the stronger source.
Organisations that cannot answer who owns a document or whether it is current should score themselves low in this category. The fix is governance, not a different embedding model.
Permissions must survive retrieval
RAG introduces a new route to information. A user who cannot open a payroll file directly should not be able to retrieve its contents through an AI assistant. Preserve source-level access controls or map them into retrieval filters that are evaluated before content reaches the model.
Test cross-role and cross-tenant scenarios deliberately. The security question is not only whether the database is private; it is whether every query is constrained by the same identity and authorization rules as the underlying systems.
Measure retrieval quality separately from answer quality
Evaluate whether the correct evidence is retrieved before evaluating the generated answer. Useful metrics include recall for known-answer questions, source precision, freshness, duplicate retrieval, permission leakage and citation completeness. A polished answer built on the wrong evidence is still a failure.
Keep an evaluation set with real business questions, expected sources and difficult edge cases. Re-run it when chunking, embeddings, ranking or source ingestion changes.
Citations and abstention are product features
Users should be able to inspect the evidence behind consequential answers. Citations need to resolve to the exact source or passage, not merely a document title. When the corpus does not support an answer, the system should say that evidence is insufficient instead of silently filling the gap.
That abstention behavior should be tested. A knowledge assistant earns trust partly through the answers it refuses to invent.
A practical readiness threshold
A strong production candidate has named source owners, clear authoritative sources, enforced permissions, measurable retrieval quality, visible citations, update pipelines, monitoring and a rollback path. If several of those are missing, pilot the system in a low-risk internal workflow first.
RAG is best treated as a governed information system with a language interface, not as a chatbot connected to a folder.
Use this framework
Use this resource as a starting point for a real engineering review. Adapt the controls, weights and thresholds to the risk, data and operating model of the system you are building.
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