BOOTH Library Adds a Verification Layer to Catch Inaccurate LLM Outputs

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A developer has released BOOTH, an open-source Python library designed to validate large language model outputs before they are passed downstream in an application. The tool addresses a common problem where LLMs produce confident-sounding answers that contradict the source evidence retrieved by a RAG pipeline. BOOTH's core function, check_with_evidence(), compares the model's response against already-retrieved documents using a user-supplied comparison function. The library is provider-agnostic, has zero runtime dependencies, and supports Python 3.9 and above. Available via pip as 'boothpy', it has recorded over 2,000 PyPI downloads and is released under the MIT license.

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