AI Evals & Observability Cheat Sheet

By the Syncfusion DevTutorials team · Last updated October 27, 2026

Every idea from AI Evals & Observability on one page: the eval loop, datasets, graders, judge calibration, statistics, CI gates, tracing, cost and latency, online evals, debugging, experiments and the LLMOps pack.

AI Evals & Observability Cheat Sheet

This page puts every idea from the course in one place.

Use it before you ship a change to an AI feature, and when production looks wrong.

Each section names the lesson that teaches it, so you can go back for the animations and exercises.

Simple lesson: you cannot improve what you have not measured. A measurement you have not checked is just a more confident vibe.

The Eval Loop

From Vibes vs Evals.

Names on Each Platform

The ideas are the same everywhere. Only the names change.

Datasets

From Datasets.

• Keep splits: an eval split you score on, and a regression split that must always pass.
• Record provenance: production, hand-written or synthetic.
• Remove duplicates, answer leaks and unrealistic synthetic cases.

Graders

From Code Graders and LLM-as-Judge and Calibration.

Judge rules: a specific rubric, temperature 0, a different model family from the app, average several runs for important scores. Trust it only when kappa with human labels is 0.6 or more and swap and padding probes do not change verdicts.

Statistics

From Statistics for Evals.

• Report every score with a 95% interval.
• Compare versions on the same cases (a paired difference).
• If the interval of the difference crosses 0, it could be noise. Do not ship on it.
• Small changes need many cases. To see a 1-point change near 92%, you need thousands of cases.

The CI Gate

From Evals in CI + v1 Export.

• Run on every pull request that changes prompts, models, retrieval or tools.
• Block on: a total below the minimum, any kind of case dropping too far, or any forbidden action.
• A total-only gate lets a big drop in one kind of case through.
• Keep the run inside a time budget. Run cases in parallel.

Tracing

From Tracing with OTel GenAI Conventions.

• One span per model call, tool call and retrieval. A root span per request.
• Put session.id on the root span. Hash user ids.
• Never put emails, card numbers or full messages with personal data in attributes.

Cost, Latency and SLOs

From Cost, Latency and SLOs.

• Cost per request = input tokens × input price + output tokens × output price.
• Watch p95 latency, not the average. The slowest users complain.
• An SLO is a target you promise, such as "p95 under 3 seconds".
• Find the expensive step in the trace before you change the model.

Online Evals and Alerts

From Online Evals and Monitoring.

• Judge a sample of live traffic. Pick the smallest rate that still catches a drift in time.
• Add user signals such as thumbs up and down.
• Alert on an average over a window, not one noisy hour.
• Every alert needs an owner and a runbook.
• Too many false alerts and people stop reading them.

Debugging and Experiments

From Debugging from Traces and Experiments for Prompts and Models.

1. Alert fires.
2. Open failing traces and compare them with good ones.
3. Find the root cause.
4. Fix it.
5. Add a regression case that fails before the fix and passes after.

• Shadow test: the new version answers live traffic, but users see the old one.
• Canary: a small share of users get the new version, behind the CI gate, with a quick rollback.
• Weigh quality against cost and latency before you promote.

Review Checklist

The LLMOps Pack

From Capstone: LLMOps Pack for Your Feature. The Export tab in the Eval Lab downloads it.