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Your LLM Evals Might Be Lying to You

This story is from 2026-10-29. It is preserved in the archive; the latest stories are on the live feed.

A model scores 92% on a benchmark, you ship it, and users complain. Sound familiar? The problem is often the evaluation , not the model. where the goal is to test software and AI agents against what users actually do, not what a leaderboard rewards. Common ways evals mislead Benchmark contamination…

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  1. 2026-10-29 12:13 · DEV Community — AI
    Your LLM Evals Might Be Lying to You

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