In our previous articles, we discussed the different types of AI evals and explored AI evaluation frameworks. In this article, we will dive into Designing & Implementing AI Evals in Practice.
Evaluating AI models isn’t just about metrics — it’s about creating repeatable, reliable processes that ensure your AI delivers real product value. Here, we’ll explore how PMs can systematically design and implement AI evals, leveraging frameworks, workflows, and tooling to measure and improve model quality effectively.
Solving the Stochastic & Non-Deterministic Nature of LLMs
Large Language Models (LLMs) are inherently stochastic, meaning they can produce different outputs even for the same input prompt. This non-determinism arises from randomness in sampling, temperature settings, and internal probabilistic modeling. While this behavior enables creativity and diversity, it poses challenges for evaluation, reliability, and product consistency.
Why It Matters for PMs
Evaluation consistency: Non-deterministic outputs make it harder to measure model quality reliably.
User experience: Users may get varying answers, which can reduce trust in critical applications.
Regression testing: Small model updates can lead to unpredictable changes in output, complicating iteration.
Practical Approaches to Mitigate Non-Determinism
Approach | How It Helps | PM Perspective |
|---|---|---|
Set a fixed random seed | Makes outputs reproducible for testing and evaluation | Ensures consistent benchmarking |
Lower temperature / reduce sampling randomness | Produces more deterministic, stable outputs | Useful for critical tasks like legal, finance, or medical queries |
Prompt engineering with constraints | Guides the model toward consistent patterns | Maintains control over format, style, and structure |
Aggregate multiple outputs | Run model several times and choose consensus or majority | Balances diversity with reliability, reduces hallucinations |
Reference-free human / LLM judgment | Evaluates quality even if outputs vary | Ensures product-level evaluation is robust to stochastic behavior |
Automated regression checks | Monitor outputs over time for consistency | Prevents silent regressions after updates |
Eliciting Labels for Metric Computation
Accurate evaluation of AI models requires ground truth labels — the “correct” answers against which outputs are compared. Label elicitation is the process of collecting these labels systematically, which is critical for both reference-based metrics and robust model evaluation.
Why Label Elicitation Matters
Reliable metrics: Without high-quality labels, your evaluation metrics (accuracy, F1, BLEU, ROUGE, etc.) may be misleading or noisy.
Edge-case detection: Proper labeling helps identify failure modes and rare scenarios that can disproportionately affect user experience.
Human-centered evaluation: For subjective outputs like summarization or chat responses, labels capture human judgment of quality, relevance, and safety.
Approaches to Elicit Labels
Approach | Description | PM Perspective |
|---|---|---|
Expert Annotation | Domain experts manually label outputs | Ensures high accuracy and reliability; ideal for critical or technical tasks |
Crowdsourcing | Use platforms like Mechanical Turk for large-scale labeling | Cost-effective for large datasets; requires careful quality control |
Consensus / Aggregation | Multiple annotators label the same item; use majority or weighted voting | Reduces bias and noise; increases confidence in metrics |
LLM-as-a-Judge | Strong LLM evaluates outputs against prompts or reference guidelines | Scales human-like evaluation; maintains consistency for subjective tasks |
Hybrid Approach | Combine human and LLM labeling | Balances scalability with domain-specific quality control |
Best Practices for PMs
Define clear labeling guidelines: Include criteria for correctness, relevance, tone, or safety.
Segment by scenario or failure mode: Helps uncover high-impact edge cases.
Monitor label quality: Use spot checks or inter-annotator agreement to maintain reliability.
Tie labels to metrics: Ensure labels directly support the evaluation metrics you care about.
Integrating Evals into Your AI Product: Offline & Online
Evaluation is not a one-time activity — for AI products to remain reliable, safe, and useful, PMs must integrate both offline and online evals into the development and operational lifecycle.
1️⃣ Offline Evaluation
Purpose: Assess model performance before deployment using curated datasets, metrics, and lab-based testing.
How it Works:
Run models on labeled or reference datasets.
Measure metrics like accuracy, F1, BLEU, ROUGE, embedding similarity, or business KPIs if historical data exists.
Include stress tests, robustness checks, and failure-mode segmentation.
Advantages:
Safe environment to catch critical bugs or biases.
Helps compare model versions and tune parameters.
Enables repeatable, systematic evaluation before impacting users.
PM Perspective:
Provides confidence in model readiness.
Identifies high-impact areas requiring iteration.
Ensures technical correctness aligns with product goals before launch.
2️⃣ Online Evaluation (Real-World Testing)
Purpose: Assess model performance in live production under real user interactions.
How it Works:
Deploy models in a controlled feature rollout or A/B testing setup.
Track metrics such as CTR, task success rate, engagement, error rates, or user-reported satisfaction.
Monitor model drift, safety issues, and edge-case behavior in the wild.
Advantages:
Measures actual product impact.
Reveals unexpected failures or gaps not seen in offline datasets.
Supports continuous improvement and iteration.
PM Perspective:
Validates whether offline metrics translate to real user value.
Provides actionable insights to prioritize updates or fixes.
Ensures AI aligns with both user expectations and business KPIs.
3️⃣ Best Practices for Integrating Evals
Combine Offline & Online: Use offline evals for safety and accuracy, online evals for real-world impact.
Segment by Use Case: Evaluate separately for critical flows vs low-impact features.
Monitor Continuously: Track drift, regression, and user feedback over time.
Automate Pipelines: Integrate metrics logging and alerting for rapid detection of issues.
AI evaluation is not just a technical task — it’s a product skill. By designing repeatable evals, eliciting high-quality labels, integrating offline and online testing, and combining foundation and application-centric evaluation, PMs can:
Identify failure modes and edge cases
Measure real user and business impact
Make data-driven product decisions with confidence
Ensure AI is safe, reliable, and aligned with strategic goals