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Developer fixes LLM token overflow bugs with accurate char/token estimates

By PulseAugur Editorial · [1 sources] ·

A developer encountered an issue where an LLM summarization task failed due to exceeding the token limit, despite a truncation mechanism being in place. The problem stemmed from two bugs: first, a shared `maxTokens` setting for a connector was incorrectly applied to both input truncation and output budget, leading to an overestimation of input allowance. Second, the character-per-token heuristic used for truncation was set too high (3.0), while the actual text, a YouTube transcript, was denser (2.91 characters per token) due to timestamps and short fragments, causing the overflow. The fixes involved implementing task-specific token budgets and using a more accurate, measured character-per-token value (2.5). AI

IMPACT Highlights the importance of accurate tokenization and input/output budgeting for reliable LLM application development.

RANK_REASON Developer describes a technical bug fix for an LLM processing pipeline.

Read on dev.to — LLM tag →

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

Developer fixes LLM token overflow bugs with accurate char/token estimates

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Developer describes a technical bug fix for an LLM processing pipeline.
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  1. dev.to — LLM tag TIER_1 English(EN) · Gabriel Le Roux ·

    Your Chars-Per-Token Estimate Is Wrong (Mine Was, By Exactly One Context Window)

    <p>A user saved a long YouTube video. The pipeline pulled the transcript — about <strong>1 MB of text</strong> — and sent it to the summarizer, which came back with:<br /> </p> <div class="highlight js-code-highlight"> <pre class="highlight http"><code><span class="err">400 Bad R…

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