Traditional monitoring watches latency and error rates. AI systems can look healthy and still be wrong: retrieval silently degrades, prompts drift, refusals spike, or cost per task doubles. Synthetic monitoring closes that gap.
Define a set of golden queries
Golden queries are a small, curated set of prompts that represent critical workflows and known edge cases. They should cover:
- High-value intents and high-frequency tasks.
- Risky intents and policy boundaries.
- RAG queries with expected sources and citation behaviour (see retrieval quality).
- Tool-driven tasks that must remain safe and correct (see tool reliability).
Score signals that map to failure modes
Synthetic tests should produce signals that map to your error taxonomy:
- Grounding signals. Citation presence and faithfulness (see grounding).
- Retrieval coverage. Expected sources appear in top results.
- Policy adherence. Refusal correctness and sensitive output scanning (see policy layering).
- Cost and latency. Token counts, retries, and stage timings (see cost anomaly detection).
Combine these with tracing so operators can see what changed when a test fails (see observability).
Run on a cadence that matches risk
Run synthetic checks:
- On every prompt/policy release (see prompt regression testing).
- On a schedule (hourly/daily) against the current production configuration.
- On provider or index changes, including re-embeddings and backfills.
Alert on trend breaks, not single failures
AI has variance. Alerting should focus on trend breaks: sustained drops in groundedness, sustained retrieval misses, or sustained cost spikes. Use SLO-style thresholds and paging rules (see SLO playbooks).
Connect failures to incident response
When synthetic monitoring fails, operators need fast levers: feature flags, routing fallbacks, context throttles, or a temporary change freeze (see incident response and change freeze).
Synthetic monitoring is how you detect regressions before users do.