What’s the minimum instrumentation needed before shipping an AI augmentation?

At minimum: (1) end-to-end `trace_id` correlation via OpenTelemetry, (2) a Prometheus latency histogram for the AI path (P95/P99), (3) a task-level success metric (not “did it return text”), (4) structured events logging model, prompt version, token usage, and fallback/guardrail outcomes.

How do you run A/B tests without risking a customer-facing hallucination incident?

Use a holdback group and a safe fallback path. Gate the AI behind feature flags, require citations/grounding for factual answers, validate outputs against strict schemas, and add a circuit breaker that forces fallback if latency/error/guardrail-block rates cross thresholds.

How do you quantify ROI when the AI feature is a copilot, not an automated decision?

Measure workflow outcomes: time-to-resolution, handle time, first-contact resolution, escalation rate, and throughput per agent. Pair that with cost-per-success (model cost divided by successful outcomes) and quality metrics (CSAT, reopens). Copilots often win on productivity—but only if latency and trust don’t degrade adoption.

What’s the most common observability mistake teams make with LLMs?

They only log prompt/response text (sometimes with PII…) and skip trace correlation. Without tying each model call to a user session, tool calls, retrieval steps, and final business outcome, you can’t debug regressions or prove lift—just argue about anecdotes.

Ai-delivery · Dec 20, 2025 · 8 minute read

The LLM Feature That “Felt Faster” (Until We Measured It and Found a 14% Conversion Drop)

If you can’t quantify the impact of AI augmentations with real instrumentation and controlled experiments, you’re just shipping vibes into production.

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The LLM Feature That “Felt Faster” (Until We Measured It and Found a 14% Conversion Drop)

Key takeaways

Implementation checklist