Offline Evaluation Plans

Why this matters

Offline evaluation plans let you predict whether a model is safe and promising before touching live users. As an AI Product Manager, you will use offline evaluation to: (1) choose the best candidate model, (2) set guardrails and launch gates, (3) align metrics with business goals, and (4) reduce risk and iteration time.

• Prioritize models for A/B tests with clear acceptance criteria.
• Prevent harmful or biased launches by enforcing offline guardrails.
• Speed up development by catching issues early via slicing and error analysis.

Who this is for

• AI/ML Product Managers driving model improvements or new AI features.
• Data/Product Analysts who evaluate model quality and business impact proxies.
• Engineers and Researchers who need a practical, product-centric evaluation framework.

Prerequisites

• Basic understanding of model types (classification, ranking, generation/LLM).
• Familiarity with common metrics (precision/recall, AUC, NDCG, BLEU/ROUGE, human ratings).
• Ability to read simple experiment dashboards and interpret metric trade-offs.

Concept explained simply

Think of an offline evaluation plan as a pre-flight checklist for your model. Before any real users experience changes, you simulate the flight in a safe, controlled environment using historical or labeled data. You compare candidates to a baseline, check vital signs (metrics), and confirm safety rules (guardrails) are met. Only then do you consider an online experiment.

Mental model

• Map: Define where you are going (business goal) and which roads to take (primary metrics).
• Flashlight: Illuminate blind spots with slices (e.g., new users, long-tail content, sensitive topics).
• Brake pedal: Guardrails that must never be violated (e.g., toxicity rate, latency, fairness).
• Ticket to fly: Acceptance criteria that earn the right to go online.

Key components of an offline evaluation plan

• Business goal and hypothesis: What outcome should improve? How will users benefit?
• Primary and secondary metrics: Choose 1–2 primaries tightly aligned to the goal; use secondaries for trade-offs.
• Datasets and splits: Representative offline data, time-aware splits, and golden sets with reliable labels.
• Baselines and candidates: Define current system (control) and new models (treatments).
• Slices and fairness checks: Segment by user type, geography, device, content category, and sensitive attributes where appropriate.
• Guardrails: Hard limits (e.g., harmful content rate ≤ X%, latency ≤ Y ms).
• Acceptance criteria: Clear thresholds to pass offline (e.g., +3% NDCG with no worse than +0.2% latency).
• Evaluation protocol: Steps to run, reviewers, and how to record decisions.
• Reproducibility: Versioned data, deterministic seeds, and saved configs so results can be replicated.

Worked examples

Example 1: Ranking recommendations for a feed

1. Goal: Increase relevant content shown to users.
2. Primary metric: NDCG@10 on a held-out recent week.
3. Secondaries: Coverage (unique items surfaced), Diversity (category entropy), Latency P95.
4. Guardrails: No increase in unsafe content rate; latency P95 ≤ baseline + 20 ms.
5. Slices: New users (≤ 7 days), heavy users, long-tail creators.
6. Acceptance criteria: +3% NDCG@10 overall and +2% on new users; guardrails met.
7. Decision: Candidate B achieves +3.6% overall, +2.4% on new users, coverage up 1.1%, guardrails met → approve for online A/B.

Example 2: Abuse/spam classifier for messaging

1. Goal: Reduce harmful messages seen by users with minimal false positives.
2. Primary metric: Cost-weighted F1 (false negatives cost 5x false positives).
3. Secondaries: Precision at operating threshold, Recall, Calibration (Brier), Latency.
4. Guardrails: False positive rate on VIP/business accounts ≤ 0.2%.
5. Slices: Language, new accounts, high-sender-volume accounts.
6. Acceptance criteria: +4% cost-weighted F1 with VIP FPR ≤ 0.2% and latency within baseline.
7. Decision: Candidate A improves cost-weighted F1 by +5.1% but VIP FPR = 0.35% → reject; adjust threshold or retrain to fix VIP slice.

Example 3: LLM assistant answer quality

1. Goal: Increase helpful, accurate responses for support questions.
2. Primary metric: Blind pairwise win rate vs. baseline on curated prompts (win/tie/loss).
3. Secondaries: Factuality (hallucination rate), Harmful content rate, Latency.
4. Guardrails: Harmful content ≤ 0.05%, hallucination rate ≤ 2% on fact-based tasks.
5. Slices: Billing issues, technical troubleshooting, refund policies, regional regulations.
6. Acceptance criteria: ≥ 60% win rate (excluding ties), hallucination ≤ 2%, harmful ≤ 0.05%.
7. Decision: Candidate C wins 63%, hallucination 1.7%, harmful 0.03% → proceed to online ramp with human-in-the-loop fallback retained.

Step-by-step plan template

1. Define outcome: Write 1–2 sentences linking the feature to a business metric.
2. Pick primary metric(s): Choose the smallest set that best represents the outcome.
3. Select datasets: Recent, representative, time-aware, plus golden sets for edge cases.
4. Set slices: Choose 5–10 meaningful dimensions; ensure sample sizes are adequate.
5. Choose guardrails: Safety, fairness, and latency thresholds you will not violate.
6. Set acceptance criteria: Thresholds vs. baseline for primary metric(s) and slices.
7. Document protocol: Who runs it, how results are reviewed, where decisions are recorded.
8. Reproduce: Save configs, seeds, versions; make results easy to re-run.

Goal & hypothesis:
Primary metric(s):
Secondary metric(s):
Datasets & splits:
Baseline & candidates:
Slices:
Guardrails:
Acceptance criteria:
Protocol & reviewers:
Reproducibility notes:
Decision & next actions:

Exercises

Complete these to practice. Then take the quick test.

1. Exercise 1: Design an offline plan for a spam classifier in a messaging app. See the exercises section below for full instructions.
2. Exercise 2: Create guardrails and acceptance criteria for an LLM help-bot. See below.

Pre-launch offline evaluation checklist

• Business goal and hypothesis are written and reviewed.
• Primary metric is tightly aligned; secondaries cover key trade-offs.
• Representative recent data plus golden sets are in place.
• Baseline performance is measured and recorded.
• Candidate performance is measured with confidence intervals where feasible.
• Key slices (including sensitive ones where appropriate) are analyzed.
• Guardrails are defined and tested.
• Acceptance criteria are met across overall and critical slices.
• Results, configs, and decisions are versioned and reproducible.

Common mistakes and self-check

Practical projects

• Backtest a recommendation model with a 4-week rolling window; report NDCG and coverage trends.
• Build a golden set for an LLM support bot including edge cases; run blind pairwise judgments vs. baseline.
• Design and run a fairness slice analysis for a classifier; propose thresholding or data fixes.

Mini challenge

You have two candidates for a content moderation model. A has higher recall but slightly worse precision; B has higher precision but worse recall. The business cost of missing abuse is 4x higher than a false positive. Write the primary metric, guardrail, and acceptance criteria you would use to pick a candidate offline.

Learning path

• Before: Understand problem framing and metric selection fundamentals.
• Now: Create solid offline evaluation plans with guardrails and slices.
• Next: Move to online experimentation, ramp plans, and post-launch monitoring.

Next steps

• Run the exercises below and write a 1-page offline plan for your current feature.
• Share it with your team for review; refine guardrails and slices.
• When ready, take the quick test. Note: The quick test is available to everyone; only logged-in users get saved progress.

Take the Quick Test

Use the Quick Test section to check your understanding and get ready for online experiments.