llm-eval-pitfalls-demo: how to find the 4% of answers that break LLM evaluation

⚡ The gist in 5 seconds - A demo repository, llm-eval-pitfalls-demo, has been published that shows how to identify the 4% of LLM answers that are critical for a company. - Requires Python 3.10+ and running the bash script./run.sh; the data is synthetic, no API keys needed. - Separately, you need access to an LLM judge (LLM-as-judge) to evaluate the answers. ### 🔍 What was found We're talking about the demo repository llm-eval-pitfalls-demo, which focuses on common mistakes made when testing neural networks. The author raises the problem of finding the small share — about 4% — of answers that can potentially cost a company dearly (the headline mentions lawsuits). To run it, you need Python 3.10+ and the ability to execute the bash script./run.sh; all data is synthetic, and no API keys are required. The demo is built as a reproducible scenario: you deploy the repository and run the pipeline via./run.sh. Among the requirements, access to an LLM judge — a model that evaluates the answers of other models — is listed separately. Modern LLM benchmarks are built on exactly such "judges," and that, judging by the description, is where the main pitfall dissected by the demo lies. ### 💡 Why it matters Evaluating LLMs via LLM-as-judge has become the standard, but it also creates blind spots: the metrics look smooth while problematic answers go unnoticed. The demo offers a practical way to find that small portion of results that actually carries risk. It's useful for engineers building internal eval pipelines who want to check the quality of their models on a reproducible synthetic dataset without spending API keys.