What Happened When I Built an Open-Source AI Project in Public

Description and use

BOOTH is a Python library that validates LLM outputs by checking parsing, ambiguity, confidence, and custom rules. After release it received 14 pull requests, sparked discussions on confidence limits, and introduced evidence‑based comparison APIs.

Problem

Developers need a way to verify LLM outputs before using them in applications, especially when confidence scores may be misleading.

Outcome

BOOTH enabled structured, evidence‑based validation of LLM outputs, attracted 14 merged PRs, and sparked community discussions on confidence calibration and evaluation APIs.

Workflow

• 1. Install BOOTH via pip
• 2. Call booth.check() or booth.acheck() to validate output
• 3. Optionally use booth.check_with_evidence() to compare against supplied evidence
• 4. Handle failures or low confidence according to application logic

Evidence

BOOTH is a lightweight, zero‑dependency, provider‑agnostic Python checkpoint library for LLM output reliability.

Source

The experiment used 300 TriviaQA questions with local models.

Source

At the time of writing, the repository has received 14 merged pull requests.

Source