Hamel Husain
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Last summary: Oct 13, 2026
7 Most Common AI Eval Mistakes (From Teaching 5K+ Engineers & PMs)
Here are the seven common mistakes to avoid when building Evals: * **Creating Evals Before Looking at Data:**
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A Deep Dive on LLM Evaluation
This talk provides an opinionated summary of challenges and best practices in evaluating Large Language Models (LLMs), drawing from research experience at Al Luther AI, a nonprofit research lab known for open-source models like GPT-J and GPT-NeoX. The speaker, a maintainer of the LM Evaluation Harness library, highlights two primary difficulties in LLM evaluation: scoring and reproducibility. Scoring is challenging because reliably assessing the correctness of natural language responses is complex. A simple keyword match is insufficient, as demonstrated by the example of answering "Who is the current US president?". A response stating "It's definitely not Joe Biden" contains the correct name but conveys the opposite meaning. This fundamental problem underscores the need for robust evaluation methods beyond human eyeballing, which is costly and inconsistent.
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