Trustworthy AI Answers: Measure What Matters
Glean
Nov 27, 2025
The value of enterprise AI isn’t in how much content it finds, it’s whether its answers can be trusted. If responses are vague or wrong, adoption stalls. This practical, five-metric framework helps you measure what matters so your AI becomes a dependable decision partner.
Why trust matters now
AI now answers, not just links, so correctness and usefulness are critical.
Leaders need evidence that AI speeds confident decisions.
A clear, repeatable framework builds trust and guides improvement.
The five metrics to measure
Accuracy – Is the answer factually correct and grounded in source material?
Score guide: 1 = wrong/hallucinated; 3 = mostly correct; 5 = fully correct with verifiable citations.
Starter KPI: ≥90% of sampled answers rated ≥4.
Relevance – Does it directly address the user’s query and context (role, permissions, project)?
Score guide: 1 = off-topic; 3 = partial; 5 = on-point with context awareness.
Starter KPI: ≥85% rated ≥4.
Coherence – Is it logically structured and easy to understand?
Score guide: 1 = confusing; 3 = readable; 5 = crisp and scannable.
Starter KPI: ≥80% rated ≥4.
Helpfulness – Did it enable the task or decision quickly (steps, links, next actions)?
Score guide: 1 = not useful; 3 = partial; 5 = clear steps with actions.
Starter KPI: ≥20% reduction in time-to-decision on benchmark tasks.
User Trust – Do employees rely on the AI as a source of truth over time?
Score guide: 1 = avoid using; 3 = cautious use; 5 = default trusted assistant.
Starter KPI: Trust NPS ≥30; rising repeat usage.
Trustworthy AI answers are responses you can rely on for work decisions. Measure them with five metrics—accuracy, relevance, coherence, helpfulness, and user trust—and track scores over time. This reveals whether your platform speeds confident decisions, where quality slips, and what to improve next.
Run a lightweight evaluation in 14 days
Build a gold set of 50–100 real tasks across teams.
Define 1–5 scoring rubrics with examples at 1/3/5 for each metric.
Recruit a mixed panel (domain experts + everyday users).
Test persona-realistic scenarios with permissions applied.
Collect scores + telemetry (citations, time-to-answer, action clicks).
Analyse by metric and function to find weak spots.
Tune and retest the same gold set to confirm gains.
What good looks like (starting benchmarks)
Accuracy: ≥90% rated ≥4; <1% hallucination rate
Relevance: ≥85% rated ≥4 with correct context
Coherence: ≥80% rated ≥4; <10% require follow-up
Helpfulness: ≥20% faster time-to-decision
User Trust: Trust NPS ≥30; rising repeat usage
From scores to action: improve fast
Optimise performance: expand/clean sources; strengthen connectors; improve retrieval.
Boost trust: require citations; show sources; track hallucination rate.
Reduce friction: standardise answer templates; add next-best actions; tailor prompts by persona.
Institutionalise learning: weekly quality reviews, a simple dashboard, and quarterly targets.
Governance & risk
Tie these metrics to your AI governance so results are auditable: policy, monitoring, incident response, and regular re-assessment after model or content changes.
FAQs
What’s the best way to measure AI answer quality?
Use a five-metric framework—accuracy, relevance, coherence, helpfulness, and user trust—and score a weekly sample 1–5 for each metric.
How many samples do we need?
Start with 50–100 tasks across teams; increase for higher-risk functions.
How do we prevent hallucinations?
Ground answers in enterprise sources, require citations, tighten retrieval/prompt constraints, and review flagged cases weekly.
Do automated checks replace human review?
No. Combine task-based human scoring with automated signals (citations present, latency, guardrails) for a complete picture.
Next steps
Request a Glean Performance Review. We’ll audit your current AI answer quality against these five metrics and deliver a focused optimisation plan.
