Evaluation and Observability
Executive view
Require golden-set thresholds and adoption metrics—not demo vibes—before scale funding.
Decision required: Go / modify / stop based on evaluation evidence?
Technical view
Cover quality, safety, ops, cost and adoption metrics; include edge and no-answer cases in golden sets.
Align OpenTelemetry / Langfuse-style traces with governance fields; redact prompts before logging.
Why traditional monitoring is not enough
Traditional systems are monitored through availability, latency, throughput and error rate.
AI systems also require monitoring of quality, groundedness, safety, bias, drift, user trust, model behaviour and token consumption.
Evaluation layers
| Layer | Purpose |
|---|---|
| Component | Retrieval, classification, extraction, tool selection, guardrails, parsing |
| End-to-end | Realistic user journeys |
| Offline | Curated datasets before deployment |
| Online | Production interactions |
| Human | Domain experts for high-risk or subjective tasks |
Core metrics
Quality — accuracy, precision, recall, F1, faithfulness, relevance, completeness.
Safety — harmful-output rate, PII-leakage rate, policy-violation rate, injection success rate.
Operations — latency, availability, error rate, retry rate, tool failure, timeout rate.
Cost — cost per request, cost per successful task, tokens per workflow, retrieval cost, infrastructure cost.
Adoption — active users, repeat usage, task completion, acceptance rate, escalation rate, user satisfaction.
Golden dataset
A golden dataset should contain representative questions, expected answers, acceptable answer criteria, required sources, disallowed content, risk category, edge cases and no-answer cases.
Observability architecture
A typical architecture may use OpenTelemetry, distributed tracing, centralised logging, metrics store, AI-specific trace platform, dashboards and alerting.
Trace request, retrieval, prompt, model, tool, response and feedback.
Grafana-based observability
A platform may use Grafana for visualisation, Tempo for traces, Loki for logs, Mimir or Prometheus for metrics, and OpenTelemetry agents and gateways.
AI-specific tools such as Langfuse can provide prompt management, LLM traces, evaluation, cost monitoring and dataset management.
Architectural separation may also be required to address licensing, isolation or operational ownership.
Case study: financial-services assistant
Offline golden set covers product facts, policy edge cases and must-refuse questions. Online: track citation accuracy, escalation rate and cost per resolved query. Traces link user → retrieval hits → prompt version → model → guardrail → feedback.
Common failure modes
- Shipping on demo vibes without a golden set
- Dashboards for latency only
- No ownership of evaluation datasets
- Tracing prompts without redaction
- Ignoring no-answer and refusal quality
Solution Engineer checklist
Solution Engineer checklist
- Evaluation layers defined for the release
- Metrics cover quality, safety, ops, cost, adoption
- Golden set includes edge and no-answer cases
- Trace schema agrees with governance needs
- Alerts mapped to human response procedures
Practical exercise
Create twelve golden items: eight factual, two adversarial, two must-refuse. Define pass/fail criteria before you look at model output.
Discussion
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