Your LLM gave you an answer. Should your application trust it?
Your LLM gave you an answer. Should your application trust it? I built BOOTH, a lightweight checkpoint layer for LLM outputs. The idea is simple: don’t automatically pass every model response downstream. Check it first. For example: Evidence: Returns are allowed within 45 days. LLM: Returns are allowed within 90 days. The answer sounds confident. It’s also unsupported by the evidence. BOOTH’s check_with_evidence() lets you check an LLM response against evidence your RAG pipeline has already retrieved. result = booth.check_with_evidence( answer=llm_answer, evidence=retrieved_docs, compare_fn=your_comparison_function, ) No need to replace your existing RAG pipeline or commit to a particular LLM provider. Zero runtime dependencies. Provider-agnostic. Small API. pip install boothpy GitHub: https://github.com/Vedantgitbot/booth How are you currently deciding whether an LLM output is safe to pass downstream? Beta · 2K+ PyPI downloads · 300+ tests · CI passing · MIT · Python 3.9+