RAG knowledge base readiness: clean sources before adding AI

Retrieval quality is limited by document ownership, structure, permissions and update discipline.

Start from the operating reality, not a technology list. Speak with the people who perform the work, and collect one successful case and one difficult case before estimating time or choosing a solution.

Turn the idea into a clear scope

Inventory authoritative sources, duplicates and conflicting versions. Split content by meaningful sections, retain document identity and attach access rules before indexing.

What to document before development

• A narrow supported task
• Approved data and knowledge sources
• Evaluation cases and refusal rules
• Human review, escalation and monitoring

For every item, name the owner, data source, successful state and user-facing failure response. These details make supplier proposals comparable and reduce hidden assumptions between client and team.

Test the exception before approving the flow

Create evaluation questions with expected evidence, including questions the system should refuse. Measure citation correctness and missing-answer behaviour, not just fluent wording.

Use realistic sample data and write the expected result as a statement someone can verify. Do not approve a screen only because it looks polished; approve it when the user completes the task and understands what happens next.

Measure whether the solution is useful

Choose a measure close to the outcome: task completion, time to confirmation, error rate, reopened requests or manual interventions. Review the number alongside user feedback because a metric alone rarely explains the cause.

Next step

Turn the idea into a reviewable brief: the goal, users, workflow, integrations and accepted outcome. You can AI solution packages, then send us the real scope for a recommendation that fits it—without public pricing or commitment before discovery.