Who this is for
AI Product Managers, analysts, and tech leads who make decisions about when to ship, how to measure quality, and how to mitigate risks in ML/AI products (classification, recommendation, generative, or decision support).
Prerequisites
- Basic understanding of ML model types (supervised, generative, retrieval-augmented, recommender systems).
- Familiarity with core metrics like precision/recall, accuracy, latency, and user satisfaction.
- Comfort reading experiment results and discussing trade-offs.
Why this matters
In real products, models fail in predictable ways. Your job is to anticipate those failures, set guardrails, and ship safely.
- Design acceptance criteria and launch gates that reflect real risk.
- Prioritize data collection and labeling to address the long tail.
- Choose metrics that match harm profiles (e.g., false negatives vs false positives).
- Plan evaluations for normal cases and out-of-distribution (OOD) cases.
- Define fallbacks and human-in-the-loop flows for high-impact errors.
Concept explained simply
Every model has limits. It learns patterns from training data and will perform best on similar data. When the world changes or the input is tricky, the model can fail. Your role is to know where those edges are, measure them, and guard users from harmful outcomes.
Mental model
Think of your AI system as four connected boxes:
- Data: what the model sees (and what it never saw).
- Model: capacity, objective, and training procedure.
- Runtime: prompts/inputs, retrieval/tools, latency/cost constraints.
- User + Feedback: decisions, incentives, and drift introduced by usage.
Failures can originate in any box or in their handoffs. Map them before they surprise you.
Common failure modes and limitations
Data and training issues
- Overfitting / Underfitting: too specific vs too simplistic.
- Data leakage: target information sneaks into features or splits.
- Label noise & class imbalance: noisy ground truth and skewed prevalence.
- Objective mismatch: optimizing proxy metrics that don’t reflect user value or risk.
- Bias in data: historical or sampling bias that harms subgroups.
Distribution shift and drift
- Covariate shift: inputs change (new slang, new fraud tactics, new camera settings).
- Concept drift: what counts as the label changes over time.
- Seasonality / non-stationarity: periodic patterns alter performance.
Runtime and system constraints
- Latency/cost limits cause timeouts, short context windows, or pruned features.
- Tool/retrieval failures: missing or irrelevant documents, stale indices.
- Cascading errors: upstream component failure breaks downstream steps.
- Non-determinism: sampling variability in generative models.
- Cold-start: no history for new users/items.
Safety and integrity
- Hallucinations / confabulations in LLMs (confidently wrong).
- Prompt injection and jailbreaking (malicious instructions in content).
- Toxicity, harassment, or policy-violating content.
- Privacy/memorization risks (regurgitating sensitive data).
- Reward hacking/goodharting in learned policies and bandits.
Feedback loops
- Popularity bias in recommenders: the rich get richer.
- Self-confirming loops: model output affects labels collected later.
- Fairness drift: performance worsens for minority segments as behavior shifts.
How to anticipate and measure failures
- Define boundaries: in-scope vs out-of-scope inputs and decisions.
- List hazards: what can go wrong and who could be harmed.
- Choose metrics per hazard: e.g., recall for safety-critical detection; calibration for decision support; groundedness for LLM QA.
- Build eval sets: IID test, OOD test (edge cases, adversarial prompts), temporal slices for drift.
- Set guardrails: thresholds, abstain/fallback behaviors, human review, rate limits.
- Monitor post-launch: drift metrics, error budgets, incident triggers, and periodic audits.
Useful metrics and checks
- Precision/Recall/PR AUC for imbalanced detection problems.
- Calibration/Brier score for probabilistic outputs and decision support.
- Latency, cost per inference, and timeout rates.
- LLM-specific: groundedness, relevance, harmfulness, refusal accuracy, instruction-following.
- Fairness: performance by segment (e.g., demographic or market slice where appropriate and lawful).
- Diversity/serendipity and de-biased CTR for recommenders.
Worked examples
1) Fraud detection classifier
Context: Very imbalanced data. High cost for missed fraud (false negatives).
- Likely failures: concept drift (new tactics), threshold too conservative, leakage from future transactions in features.
- Metrics: recall at fixed precision, PR AUC, calibration; decision cost curve.
- Mitigations: frequent retraining, drift monitors on key features, dynamic threshold by segment, human review for high-uncertainty cases.
- Launch gate: chargeback rate must not exceed baseline; recall on OOD set must be within X% of IID.
2) LLM customer support assistant (RAG)
Context: Answers must be grounded in internal policies.
- Likely failures: hallucinations, prompt injection from user content, retrieval misses, toxic tone in edge cases.
- Metrics: groundedness, answer relevance, harmfulness, refusal accuracy when uncertain, retrieval hit rate.
- Mitigations: retrieve-then-answer, low-confidence abstain, content filters, system prompts that prioritize policy quotes, timeouts with safe handoff to human.
- Launch gate: zero known critical policy violations on red-team set; groundedness ≥ target on OOD queries.
3) Marketplace recommender
Context: Want conversion while maintaining fairness and catalog health.
- Likely failures: popularity bias, cold-start for new sellers, feedback loop reinforcing a narrow set.
- Metrics: de-biased CTR, conversion, exposure fairness across seller segments, diversity at top-k.
- Mitigations: exploration (epsilon-greedy or UCB), exposure caps, re-ranking for diversity, periodic counterfactual evaluation.
- Launch gate: minimum exposure guarantee for new items while maintaining conversion within tolerance.
4) Visual defect detection in manufacturing
Context: Camera upgrade introduces new lighting and angles.
- Likely failures: covariate shift, over-reliance on background cues, class imbalance for rare defects.
- Metrics: recall at low false positive rate; per-defect calibration.
- Mitigations: augmentations matching new setup, domain adaptation, separate OOD acceptance tests before enabling auto-rejects.
Quick checklist
- Have we named high-impact failure modes and their owners?
- Do we have IID, OOD, and red-team test sets?
- Are thresholds tied to business cost of errors?
- Is there a safe fallback/abstain path?
- Do we monitor drift and segment performance?
- Is a rollback plan documented?
Exercises
Complete the exercise below. You can take the quick test anytime; progress is saved only for logged-in users, but the test is available to everyone.
Exercise 1: Map risks for a medical triage assistant
Scenario: You manage an LLM-based triage assistant that asks patients about symptoms and suggests next steps. It uses retrieval over vetted clinical guidelines. High stakes: unsafe advice is unacceptable.
- List 5 likely failure modes for this system.
- For each, pick 1–2 metrics or checks you will use (e.g., harmfulness, groundedness, abstain accuracy).
- Define a mitigation or guardrail (e.g., mandatory human review triggers, refusal for red-flag symptoms).
- Propose a launch gate for critical risks.
Show a sample solution structure
Example outline:
- Failure: Hallucination → Check: groundedness on OOD set → Mitigation: answer only with retrieved content; otherwise abstain → Gate: 0 critical hallucinations on red-team set.
- Failure: Missing red flags → Check: recall on symptom red-flag set → Mitigation: rules for auto-escalation → Gate: ≥ 99% recall on critical symptoms.
- Failure: Prompt injection → Check: adversarial prompt test pass rate → Mitigation: input sanitization; strict system prompt → Gate: ≥ 98% pass rate.
- Failure: Toxic/biased responses → Check: toxicity rate → Mitigation: safety filter → Gate: 0 severe toxicity cases.
- Failure: Retrieval miss → Check: retrieval hit@k → Mitigation: index refresh; fallback to standardized triage flow → Gate: hit@k ≥ target on eval set.
Common mistakes and self-check
- Mistake: Using accuracy for highly imbalanced problems. Self-check: Do we report PR AUC and cost-weighted metrics?
- Mistake: Only testing on IID data. Self-check: Do we have OOD and adversarial sets?
- Mistake: No abstain/fallback. Self-check: Are low-confidence cases handled safely?
- Mistake: Ignoring calibration. Self-check: Are probabilities reliable; do we monitor Brier score?
- Mistake: Shipping without rollback plan. Self-check: Can we disable model quickly if incidents occur?
Practical projects
- Create a risk register and evaluation plan for one of your products. Include hazards, metrics, datasets, gates, and owners.
- Build a red-team set for your LLM or classifier (50–100 cases). Tag each with expected outcome and severity.
- Simulate drift: train on past data, test on a recent time slice. Document performance deltas and propose update cadence.
Learning path
- Before this: Basics of model types and evaluation metrics.
- Now: Model limitations and failure modes (this lesson).
- Next: Guardrails, human-in-the-loop design, and incident response.
Next steps
- Write a one-page risk brief for a current feature; agree on launch gates with engineering and legal/safety stakeholders.
- Schedule a monthly drift audit and post-incident review template.
- Run the quick test below to check your understanding.
Mini challenge
Pick one failure mode in your product that worries you most. In 10 minutes, draft: the detection signal, the mitigation, and the rollback trigger. Share it with your team and ask, “What did I miss?”