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Developer uses code for LLM scoring to ensure accuracy

By PulseAugur Editorial · [1 sources] ·

A developer building a platform for German B1/B2 exam preparation found that Large Language Models like Gemini are unreliable for precise calculations such as word counts and scoring arithmetic. To ensure accuracy, the developer implemented a system where the LLM handles language-based grading and feedback, while custom code performs all computational tasks. This split approach validates LLM outputs and guarantees that objective scoring rules are applied correctly, preventing errors that are hard to detect in LLM-generated results. AI

IMPACT Highlights the need for hybrid systems where LLMs handle language tasks and code handles precise calculations for reliable AI applications.

RANK_REASON Developer describes a specific implementation detail for an AI-powered tool.

Read on dev.to — LLM tag →

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

Developer uses code for LLM scoring to ensure accuracy

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17 / 100
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Tool
Developer describes a specific implementation detail for an AI-powered tool.
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product, other
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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) · Nikolai | ExamReady ·

    Don't let the LLM do the maths: grading writing with AI but scoring in code

    <p>If you ask a language model to grade a text and also to count the words, add up the points and apply the scoring rules, it will usually give you a confident answer. Sometimes the answer will be wrong, and you won't notice, because the output looks reasonable.</p> <p>I ran into…

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