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LLM judge catches 100% of errors after prompt change

By PulseAugur Editorial · [1 sources] ·

A developer tested two local Ollama models, llama3.2:1b and llama3.1:8b, to evaluate an LLM's ability to grade math problems. Initially, the judge model failed to identify any of the 41 deliberately incorrect answers when simply asked to verify the provided solution. However, after modifying the prompt to instruct the judge model to solve the problem itself before comparing its solution to the candidate answer, the judge model successfully identified all 41 incorrect answers. AI

IMPACT Prompt engineering can significantly improve the accuracy of LLM judges, making them more reliable for evaluating AI-generated content.

RANK_REASON The item describes an experiment and its findings regarding LLM evaluation techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM judge catches 100% of errors after prompt change

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51 / 100
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The item describes an experiment and its findings regarding LLM evaluation techniques. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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model release, product
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Priyansh Singhal ·

    My LLM Judge Caught 1 of 41 Wrong Answers. One Line Fixed the Rest.

    <p>Asking the judge to solve the problem itself before grading turned a rubber stamp into a real check.</p> <p>I built a small local judge to grade a weaker model's math homework. I fed it 41 answers I had deliberately made wrong, expecting it to catch most of them. It caught one…

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