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

Evaluation Framework Design

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

Hiring signal: Every AI company in 2026 tests eval engineering: candidates who can structure inner loop (development, small eval set, fast iteration) and outer loop (production, comprehensive, regression detection) evaluation pipelines pass. Candidates who build AI systems without evaluation fail. The metric taxonomy (task-specific, operational, safety) and eval set construction methodology are expected knowledge at OpenAI, Anthropic, and every AI startup.

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

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 Inner Loop Evaluation: Fast Dev Feedback, Outer Loop Evaluation: Production Regression Detection, Metric Taxonomy, Eval Set Construction, Evaluation Cadence, Practical Application, What Hiring Managers Look For, Resources, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.

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