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Developer finds prompt bias skewed LLM receipt scanning tests

By PulseAugur Editorial · [1 sources] ·

A developer tested several large language models for a receipt scanning application, finding that Google's Gemini 3.5 Flash, despite its higher cost, provided accurate results. Initial tests with DeepSeek's V4 models were inconclusive due to API limitations, and Qwen3-VL-32B, while cheaper, failed to reconcile receipt totals accurately, showing a significant discrepancy. The developer discovered that their own prompt tuning for Gemini had inadvertently biased the tests, leading to an inaccurate initial assessment of Qwen's performance. AI

IMPACT Highlights the importance of unbiased testing and prompt engineering when evaluating LLM performance for specific tasks.

RANK_REASON Developer's personal experience and testing of LLMs for a specific application.

Read on dev.to — LLM tag →

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

Developer finds prompt bias skewed LLM receipt scanning tests

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Developer's personal experience and testing of LLMs for a specific application.
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68 days old
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Damien Alleyne ·

    My Code, My Test, and My Prompt All Agreed. All Three Were Wrong.

    <p>A friend handed me a grocery receipt to test my receipt-scanning app, <a href="https://app.bankinginbim.com" rel="noopener noreferrer">Receipt Tracker</a>, which turns a photo into categorised line items. It was a dense Massy Stores run, 37 items, and the photo wasn't the shar…

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