How to reduce wrong answers from an enterprise AI assistant

Separate retrieval failures from answer failures

When an internal AI assistant gives a convincing but wrong answer, changing the prompt may not address the cause. The right document may be absent, an old version may outrank the current policy, or the assistant may generate a claim the retrieved evidence does not support. Inspect what was retrieved as well as what was answered.

Build an evaluation set from real work

Ask the responsible team for representative questions and approved answers. Include ambiguous requests, questions with no approved answer, conflicting policies and queries from users with different access rights. Record the source that supports each expected answer and the circumstances where escalation is required.

• Measure whether the relevant source appears in retrieval results.
• Check whether every consequential claim has supporting evidence.
• Test whether restricted documents remain inaccessible to unauthorised users.
• Include outdated files, tables, abbreviations and scanned documents.

Improve the knowledge boundary

Assign owners to source collections and remove or clearly mark obsolete versions. Preserve section context when splitting documents for retrieval. Use permissions at retrieval time, not just in the interface. Where different business units use the same term differently, ask a clarifying question or constrain the source collection. A citation helps the reader inspect an answer but does not prove the answer is correct.

Make uncertainty useful

Give the assistant a clear fallback when evidence is missing or conflicting. It can identify the missing source, explain that it cannot verify an answer and route the question to the responsible team. Do not let a knowledge assistant silently become an agent that changes business records. Actions need separate permissions, approval boundaries and an audit trail.

Agree release criteria before expanding

Measure answer correctness, evidence support, permission isolation, useful escalation, latency and cost. Review failures by category rather than averaging them away. Keep a regression set and rerun it when a model, source collection or retrieval configuration changes. SOFT TASK India can help define the evaluation process, connect approved knowledge and scope a pilot with accountable ownership.

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