Why it matters
Explainability helps users and reviewers understand why an answer appeared. In knowledge systems, explainability often means showing sources, policy refusals, and confidence boundaries rather than neuron diagrams.
How it works
Surface citations, retrieval summaries, refusal reasons, and escalation options. Train support staff to read traces. Avoid fake certainty on uncertain topics.
Example
Nintendo explainability UI shows cited Refund Policy excerpts and notes when answers exclude internal-only details for security reasons.
Common mistakes
- 1Explainability theater with generic because the model thought text
- 2Hiding refusals behind unhelpful error messages
- 3Overwhelming users with raw chunk dumps
Test the answer before your customer does.
Run answer tests and evaluations against your governed knowledge before you ship.
See answer evaluations