LLM gave you an answer. Should your application trust it?

LLM gave you an answer. Should your application trust it? Vedantgitbot / booth Public Notifications You must be signed in to change notification settings Fork 0 Star 8 Branches Tags Open more actions menu Latest commit History 48 Commits 48 Commits Folders and files Name Name Last commit message Last commit date .github .github assets assets examples examples src/ booth src/ booth tests tests .gitignore .gitignore CHANGELOG.md CHANGELOG.md CODE_OF_CONDUCT.md CODE_OF_CONDUCT.md CONTRIBUTING.md CONTRIBUTING.md LICENSE LICENSE README.md README.md TUTORIAL.md TUTORIAL.md USECASES.md USECASES.md pyproject.toml pyproject.toml Repository files navigation BOOTH A lightweight checkpoint layer for AI. Should my application trust this LLM output?

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• Vedantgitbot / booth Public Notifications You must be signed in to change notification settings Fork 0 Star 8 Branches Tags Open more actions menu Latest commit History 48 Commits 48 Commits Folders and files Name Name Last commit message Last commit date .github .github assets assets examples examples src/ booth src/ booth tests tests .gitignore .gitignore CHANGELOG.md CHANGELOG.md CODE_OF_CONDUCT.md CODE_OF_CONDUCT.md CONTRIBUTING.md CONTRIBUTING.md LICENSE LICENSE README.md README.md TUTORIAL.md TUTORIAL.md USECASES.md USECASES.md pyproject.toml pyproject.toml Repository files navigation BOOTH A lightweight checkpoint layer for AI.
• Should my application trust this LLM output?
• Tutorial · Use Cases · Changelog · Contributing · Issues Every app built on an LLM call has to answer that question eventually, usually the hard way, after a confidently wrong answer has already reached a user.
• BOOTH is the checkpoint that answers it first.
• It sits between your application and an LLM call, and hands you back a structured, defensible decision instead of just whatever fluent text the model gave you.
• pip install boothpy import booth result = booth .