If you’re a premium subscriber
Add the private feed to your podcast app at add.lennysreads.com
In this episode, you’ll learn:
Why most AI eval dashboards fail to deliver real product improvements
How to use error analysis to uncover your product’s most critical failure modes
The role of a “principal domain expert” in setting a consistent quality bar
Techniques for transforming messy error notes into a clean taxonomy of failures
When to use code-based checks vs. LLM-as-a-judge evaluators
How to build trust in your evals with human-labeled ground-truth datasets
Why binary pass/fail labels outperform Likert scales in practice
Evaluation strategies for complex systems: multi-turn conversations, RAG pipelines, and agentic workflows
How CI safety nets and production monitoring work together to create a flywheel of continuous product improvement
References:
