Mastering AI Evals: A Guide for Product Managers and Engineers, Get insights gained from helping over 30 companies, highlighting that unsuccessful AI products almost always fail due to a lack of robust evaluation systems. Successful teams, in contrast, obsess over measurement and iteration, enabled by these evaluation systems.
Discover how evals are a critical element of any AI initiative, preventing product failure and accelerating iteration velocity.
Explore the AI Evals Flywheel, presented as a virtuous cycle crucial for differentiating great from mediocre AI products, connecting evaluation, debugging, and changing product behaviour.
The discussion covers the three essential levels of AI evaluation:
Level 1: Unit Tests (Assertions): These are fast and cheap, ideal for running on every code change to get quick feedback. They should be organised beyond typical unit tests and frequently updated based on observed failures.
Level 2: Model & Human Eval: This level is for deeper validation, requiring logging traces and human feedback. Learn the importance of removing friction from looking at data, using binary ratings for simplicity, and tracking correlation between model and human evaluation to decide how much you can rely on automation.
Level 3: A/B Testing: This is the most costly level, typically reserved for more mature products and significant changes to ensure the AI product drives desired user outcomes or behaviours.Learn about the more effective bottom-up approach to AI Eval metrics, focusing on discovering domain-specific failure modes by looking at actual data and letting metrics naturally emerge, rather than starting with generic top-down metrics. Hamel uses real-world examples, like Rechat's AI assistant Lucy and NurtureBoss, which used a bottom-up approach to identify key issues accounting for over 60% of their problems.
Finally, uncover the three free superpowers that robust evaluation systems unlock: Fine-Tuning (primarily via preparing high-quality data), Data Synthesis & Curation (leveraging existing eval infrastructure to filter and curate data, often synthetically generated using LLMs), and streamlined Debugging (due to the significant overlap between the infrastructure needed for evaluation and debugging).
Tune in for practical takeaways, including tips on simplifying your approach, looking at lots of data, and using LLMs to generate tests, synthetic data, and critiques. This episode provides essential insights for anyone building AI products, focusing on the most impactful investment you can make: your evaluation system.
