"Explainable AI" is often discussed as a research topic. In production, explainability is more practical: can you tell a user why an answer was refused, why a tool action was blocked, or why a particular source was used?
In modern LLM systems, the best explainability pattern is a combination of evidence (citations), reason codes, and user-friendly messages.
Start with the common explainability questions
Users and auditors tend to ask predictable questions:
- Why did you refuse or limit the answer?
- What sources did you use and why those sources?
- Why did you choose this model or route?
- Why was a tool action denied or stepped up to approval?
Use evidence-first explanations
For RAG and knowledge answers, citations are the most powerful explainability mechanism. Require citations for key claims and surface source metadata like freshness and owner where appropriate (see citations and grounding and source quality scoring).
Introduce reason codes aligned to the system
User-facing messages should map to system decisions. If the system applies a policy pack, truncates context, or denies a tool call, record a reason code and show a safe, simplified explanation to the user (see decision logging).
Examples of reason-code categories:
- Policy. Disallowed content, restricted data classification, or region constraints.
- Evidence. Not enough trusted sources to answer safely.
- Cost/latency. System is in bounded mode to protect performance.
- Tool safety. Action requires approval or stronger identity checks.
Keep explainability from becoming a leak
Explainability should not mean exposing system prompts or internal control text. Use safe, user-oriented messages and avoid printing hidden instructions (see prompt confidentiality).
Make it operational
Explainability improves when it is measurable. Track:
- Refusal rates by reason code.
- User escalation and dissatisfaction by reason code.
- Incidents linked to missing or misleading explanations.
These signals belong in dashboards and runbooks (see safety dashboards and alerting and runbooks).
Explainability is not a layer you bolt on. It is a product and governance feature that turns AI behaviour into something users can understand and trust.