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LLM reasoning leaks can corrupt JSON output, author finds

By PulseAugur Editorial · [1 sources] ·

A common issue when extracting JSON from large language models, particularly when the model includes its reasoning process within <think> tags, is that standard JSON parsers can fail or return incorrect data. Even specialized libraries like `json-repair` can incorrectly parse incomplete or malformed JSON drafts found within these reasoning blocks. The author proposes a solution that involves first removing any <think> blocks, including incomplete ones, before attempting to parse the remaining text for the last complete JSON object. AI

IMPACT Highlights a practical challenge in integrating LLMs into applications requiring structured data.

RANK_REASON Article details a specific technical problem and solution for parsing LLM output.

Read on dev.to — LLM tag →

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

LLM reasoning leaks can corrupt JSON output, author finds

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Article details a specific technical problem and solution for parsing LLM output.
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  1. dev.to — LLM tag TIER_1 English(EN) · ToNBi ·

    When a reasoning model's <think> block leaks a JSON draft into your parser

    <p><em>Disclosure: I wrote this article together with an AI assistant (Claude). The AI ran all the code below and checked the results. I am not a professional programmer, so please read it with that in mind.</em></p> <h2> The problem </h2> <p>When you pull JSON out of an LLM's re…

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