AI Evaluation Strategy

AI Evaluation Strategy

Move beyond vibe checks to systematic, empirical measurement of AI product quality and reliability.

Help the user with ai evaluation strategy using insights from 11 guests and posts across Lenny's Podcast and Newsletter.

How to Help

1. Identify Failure Modes - Help the user conduct error analysis on real traces to find where the system specifically breaks.
2. Select Eval Methods - Recommend the right mix of human, code, and LLM judges based on the specific technical use case.
3. Build Gold Sets - Assist in curating a reference dataset of high-quality examples to act as the ground truth for your application.
4. Operationalize - Guide the user in integrating these evaluations into a CI/CD pipeline for continuous quality improvement.

Core Principles

Automate the Value Chain

Brendan Foody: "I think that for enterprises especially, the core way to think about it is how can they build a test or systematic way to measure how well AI automates their core value chain? So if it's an architecture firm that's producing these architecture diagrams of what they provide to their end customer, how can they effectively measure that? And each company has its own value chain or maybe a handful of them if it's a multi-product company."

Identify the core deliverables unique to your business and develop systematic tests to measure how accurately AI can replicate those specific tasks.

Prioritize Subjective Excellence

Edwin Chen: "We are looking for a Nobel Prize-winning poetry. Is this poetry unique? Is it full of subtle imagery? Does it surprise you and target your heart? Does it teach you something about the nature of moonlight?"

True data quality is defined by deep, subjective human excellence, such as emotional resonance and uniqueness, rather than superficial binary checks.

Eliminate Vibe Checks

Hamel Husain & Shreya Shankar: "Evals help you create metrics that you can use to measure how your application is doing and kind of give you a way to improve your application with confidence. That you have a feedback signal in which to iterate against."

Create systematic metrics to track application quality over time, allowing teams to iterate on prompts or models with the same confidence as traditional software.

Structured Judge Logic

From "Beyond vibe checks: A PM’s complete guide to evals": "Clearly articulating what you want your judge-LLM to measure isn’t just a step in the process; it’s the difference between a mediocre AI and one that consistently delights users. Building these writing skills requires practice and attention."

Write effective automated evaluations by using a structured prompt that defines the role, data, success criteria, and specific labels for the judge.

Templates & Frameworks

• LLM-as-a-Judge Playbook (Building eval systems that improve your AI product) - A systematic three-step process for building, validating, and measuring an LLM judge that provides trusted binary pass/fail metrics for subjective AI quality as
• Three Eval Approaches (Human, Code-based, LLM-based) (Beyond vibe checks: A PM’s complete guide to evals) - A decision framework for choosing the right eval approach based on your use case, with pros and cons for each.
• The Eval Formula (Four-Part Structure) (Beyond vibe checks: A PM’s complete guide to evals) - A four-part formula for writing effective LLM-based eval prompts that any PM can use to construct judge-LLM prompts.