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

> Source: <https://dev.to/vedant_brahmbhatt_7be1822/your-llm-gave-you-an-answer-should-your-application-trust-it-40fe>
> Published: 2026-09-29 21:32:24+00:00

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](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+