AI research: evaluating retrieval-augmented enterprise assistants
Assess grounded answers, source access and failure modes before deploying a knowledge assistant.
Prepared on 18 Oct 2026 for editorial review. Product specifications and company research results are not asserted unless verified.
Define the research question
Retrieval-augmented generation combines document search with language generation. A useful research question is whether the assistant can answer representative business questions using approved sources and abstain when evidence is insufficient. Start with a curated evaluation set drawn from actual information needs.
Separate retrieval failures from generation failures. If the right document is not retrieved, prompt changes alone are unlikely to solve the problem. Inspect indexing, document structure, metadata and search relevance before expanding the model’s instructions.
Test evidence and access
Evaluate whether each material claim is supported by a cited passage, whether citations point to the correct version and whether contradictory documents are handled explicitly. Include questions that cannot be answered from the corpus to test appropriate abstention.
Document access must be enforced before retrieval and checked throughout the workflow. Test users with different permissions, deleted documents and untrusted instructions embedded in content. A useful answer is not acceptable if it exposes information the requester should not see.
Report limitations clearly
Measure supported-answer rates, retrieval relevance, latency and review effort. Keep evaluation data distinct from tuning data and record the model, prompt and index versions. This article outlines a research method; it does not report completed experiments or claimed KaribuTech benchmark results. Publish findings only when the evidence and limitations can be reviewed.