September 20, 2026
A high benchmark score does not predict production: GitHub outlines its evaluation practices
On 19.09.2026, GitHub published its practices for evaluating language models: a high score on a clean benchmark does not protect against failure on the cases that matter in real work.
GitHub
@github
A language model can perform brilliantly on a clean benchmark and still stumble on cases that matter in real work. Here are the evaluation practices that helped us get from promising prototype results to production. ✅ https://github.blog/ai-and-ml/llms/how-to-evaluate-llms-before-production/
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On 19.09.2026, GitHub published its practices for evaluating language models: a high score on a clean benchmark does not protect against failure on the cases that matter in real work.
The full breakdown of the practices is on the GitHub blog.
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