Evaluation And Experimentation

Why this matters for AI Product Managers

Evaluation and experimentation let you ship AI confidently. As an AI PM, you define what “good” means, prove value offline before risking users, run safe online tests, and monitor quality over time. This skill unlocks faster iteration, safer launches, and trustworthy AI outcomes.

• Translate product goals into measurable AI metrics
• Design offline tests to de-risk launches
• Run ethical, statistically sound experiments
• Set guardrails and quality gates to prevent harm
• Monitor quality drift and trigger retraining or rollbacks

Who this is for

• AI Product Managers owning ML/LLM features (recommendations, ranking, search, moderation, assistants)
• PMs/Leads transitioning from traditional product to AI features
• Startup founders validating AI product value quickly and safely

Prerequisites

• Comfort with basic product metrics (conversion, retention, latency)
• Basic statistics (mean, variance, confidence intervals, p-values)
• Familiarity with ML/LLM concepts (classification, ranking, generative responses)

Learning path

Worked examples

# Simple metric check in Python
tp, fp, fn = 80, 4, 35
precision = tp / (tp + fp)
recall = tp / (tp + fn)
print(round(precision,3), round(recall,3))

# Aggregate rubric scores and guardrails
scores = [
  {"correct":4,"ground":4,"clar":5,"safe":5},
  {"correct":3,"ground":2,"clar":4,"safe":5},
]
mean_correct = sum(s["correct"] for s in scores)/len(scores)

# Simple PSI-like drift check for a binned feature
prev = [0.1,0.2,0.3,0.4]
cur  = [0.05,0.25,0.35,0.35]
psi = sum((c-p)* (0 if c==0 or p==0 else ( (c/p) and __import__('math').log(c/p) )) for p,c in zip(prev,cur))
print(psi)  # placeholder calc; flag if > 0.25

Drills and quick exercises

• Write a one-sentence OEC for your AI feature and 3 guardrails.
• Draft an offline plan: datasets, labels, metrics, acceptance thresholds.
• Specify randomization unit, MDE, and duration for an upcoming A/B test.
• Define a HITL routing rule using confidence and risk.
• List 5 monitoring metrics for day-2 operations.

Common mistakes and debugging tips

• Using accuracy on imbalanced data: Prefer recall/precision, ROC/PR curves, or recall at fixed precision.
• Peeking during experiments: Increases false positives. Use fixed-horizon or proper sequential methods.
• Metric drift from data leakage: Verify data splits and labeling windows; re-run with time-based splits.
• Ignoring subgroup performance: Track fairness slices and enforce maximum allowed gaps.
• Launching without guardrails: Define toxicity, bias, latency, and error-rate gates before ramping.
• No runbook for incidents: Predefine rollback triggers, owners, and comms.

Practical projects

• Build and evaluate a moderation classifier: define OEC, offline plan, HITL, and a simulated A/B.
• LLM support assistant: create a 4-criteria rubric, guardrails, and run a shadow test.
• Ranking improvement: design a user-level A/B with staged ramp and guardrail dashboard.

Mini project — Ship a safe AI reply suggestion feature

Subskills

• Defining Success Metrics For AI — Turn product goals into OEC, leading indicators, and guardrails with clear thresholds.
• Offline Evaluation Plans — Datasets, labels, metrics, cost matrix, and analysis plan to de-risk launches.
• Online Experiment Design Basics — Hypotheses, randomization, power/MDE, duration, and stopping rules.
• A B Testing For AI Features — Safe ramps, guardrails, analysis, and documenting learnings.
• Human In The Loop Evaluation — Rubrics, inter-rater reliability, routing thresholds, and audits.
• Guardrail Metrics And Quality Gates — Toxicity, bias, latency, PII leakage; enforce blocking thresholds.
• Monitoring Quality Over Time — Dashboards, drift detection, alerts, and retraining triggers.

Next steps

• Work through each subskill below, then take the skill exam.
• Apply the mini project to your product area and share results with your team.
• Schedule a monthly review of guardrails and monitoring alerts to keep quality high.