Phase 7: AI Evaluation & Observability · 55 min · Python · LangSmith · Braintrust

Evaluation Framework Design

Eval engineering is the 2026 non-negotiable. No evals, no production.

What you will learn

Structure inner loop evaluation: rapid development feedback, small eval sets, fast iteration
Structure outer loop evaluation: comprehensive production traces, regression detection, A/B testing
Define metric taxonomy: task-specific (accuracy, faithfulness), operational (latency, cost), safety (toxicity, PII)
Construct eval sets: representative queries, edge cases, adversarial inputs, golden datasets
Determine evaluation cadence: per-commit, per-deployment, continuous production

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Structure inner loop evaluation: rapid development feedback, small eval sets, fast iteration
Structure outer loop evaluation: comprehensive production traces, regression detection, A/B testing
Define metric taxonomy: task-specific (accuracy, faithfulness), operational (latency, cost), safety (toxicity, PII)
Construct eval sets: representative queries, edge cases, adversarial inputs, golden datasets
Determine evaluation cadence: per-commit, per-deployment, continuous production

The Problem

Evaluation engineering is the 2026 non-negotiable for AI systems. No evals, no production. An evaluation framework defines what you measure, how you measure it, what the thresholds are, and what happens when a metric drops below threshold. This lesson covers metric selection, test set construction, automated evaluation pipelines, and gating criteria.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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