Evals are shifting from an obscure machine learning concept to a fundamental skill for building successful AI products. This is driven by the need to move beyond subjective 'vibe checks' to a systematic, data-driven methodology for measuring and improving the performance of non-deterministic AI systems.
A key technique discussed is using an 'LLM as a judge' to automate evaluations. This involves creating a highly-scoped prompt that asks an LLM to assess a specific, narrow failure mode and return a simple binary (pass/fail) output, effectively scaling the judgment of a human expert.
Despite the goal of automation, the entire evaluation process is rooted in human expertise. It begins with a domain expert (a 'benevolent dictator,' often the PM) manually analyzing data to define what 'good' looks like, and this human-labeled data serves as the ground truth for validating any automated 'LLM judge'.
The speakers emphasize that doing evals correctly requires discipline. Common pitfalls include using ambiguous multi-point rating scales instead of binary outputs and blindly trusting an LLM judge without validating its performance against human labels using a confusion matrix.
Keep pulling the thread on Hamel Husain & Shreya Shankar.