Building eval systems that improve your AI product

09/09/25
Source: Lenny’s Newsletter by Hamel Husain. Read the original article

TL;DR Summary of Building Effective AI Evaluation Systems: A Practical Playbook for Product Improvement

This guide reveals a comprehensive methodology for AI product evaluation that moves beyond superficial metrics to address real user problems through rigorous error analysis. It emphasizes appointing a domain expert to conduct detailed open and axial coding, enabling the identification of key failure modes. The playbook outlines building reliable automated evaluators using both code-based checks and LLM judges, validated via human-labeled datasets to ensure trustworthy continuous improvement. Special strategies for complex AI systems like multi-turn conversations, retrieval-augmented generation, and agents are also covered to help optimize performance effectively.

Optimixed’s Overview: Mastering AI Product Evaluation to Drive Meaningful Improvements

Introduction to AI Evaluation Challenges

Many AI teams fall into the trap of relying on generic metrics such as hallucination or toxicity scores, which often do not correlate with actual user pain points. This playbook guides product managers and engineers through a structured approach that starts with understanding how an AI product fails in real-world contexts, rather than what is easy to measure.

Phase 1: Discovering What to Measure Through Error Analysis

  • Designate a Principal Domain Expert: Assign a single expert who understands your product’s domain deeply to act as the arbiter of quality, ensuring consistent and informed judgments.
  • Sample User Interactions: Begin with a random set of approximately 100 interactions and document detailed critiques (open coding), noting pass/fail decisions and failure causes.
  • Group Failures (Axial Coding): Identify common failure patterns and prioritize the most impactful categories to focus your evaluation efforts.
  • Leverage Off-the-Shelf Metrics Creatively: Use metrics like hallucination scores not as direct KPIs but as tools to surface unexpected failure modes by examining extremes.

Phase 2: Building a Reliable Evaluation Suite

The goal is to create evaluators trusted by your team. Choose between:

  • Code-based Evaluators for objective, rule-driven checks (e.g., JSON validity, presence of required keywords).
  • LLM-as-a-Judge for subjective assessments requiring nuanced judgment (e.g., tone appropriateness, relevance), which must be rigorously aligned and validated against human-labeled ground truth data.

Validation involves splitting data into training, development, and test sets to prevent overfitting and measuring true positive and true negative rates to ensure balanced performance. This process ensures your evaluation metrics reflect reality and build stakeholder trust.

Phase 3: Operationalizing Continuous Improvement

With a validated evaluation suite, you can implement a continuous improvement flywheel that catches regressions before deployment. This requires integrating diagnostics into workflows and adapting evaluations to complex AI architectures:

  • Multi-turn Conversations: Focus on session-level pass/fail outcomes and isolate failures to individual turns to diagnose conversational memory versus knowledge issues.
  • Retrieval-Augmented Generation (RAG): Evaluate retrievers separately using recall@k metrics and generators for faithfulness and relevance, prioritizing retriever quality before generation improvements.
  • Agentic Workflows: Use transition failure matrices to pinpoint exact steps where complex agent workflows break down, enabling targeted debugging.

Conclusion

This playbook transforms AI evaluation from a vague, superficial exercise into a rigorous, data-driven discipline. By centering your efforts on real failures and validating your metrics with expert judgment, you create a trusted system that powers meaningful product enhancements and sustainable growth.