Observability gives you the lens to see how AI features behave: inputs, outputs, latency, cost, and quality signals.

Observability for AI Features

Observability is the ability to understand internal behavior from external outputs and traces. In AI, this means you can answer: What was asked? What did we retrieve? What did the model produce? Was it safe?

Key Signals

**Prompt & Parameters** – The exact inputs sent to the model.
**Retrieved Context** – Documents or snippets fed into generation (RAG).
**Latency & Tokens** – Performance and cost drivers.
**Safety Events** – Blocks, warnings, classifier scores.
**User Feedback** – Explicit ratings or downstream conversions.

Why It Matters

Without observability, improving model quality or diagnosing failures becomes guesswork. With it, you can run evaluations, A/B test prompts, and justify changes.

Practical Steps

1. Log each request with a trace ID.

2. Store structured metadata (JSON) for searchability.

3. Add lightweight dashboards for volume, errors, and outliers.

4. Feed insights back into prompt/version iteration.

Good observability turns AI from a black box into an improvable product surface.