THE AI TRIAGE PIPELINE: FROM GITHUB WEBHOOKS TO GROQ LLMS
Welcome to AI Triage. Welcome to AI Triage. Welcome to AI Triage. Welcome to AI Triage. Welcome to AI Triage. Welcome to AI Triage. Welcome to AI Triage.
Let’s be honest: we’re a little embarrassed. Our bot is out there triaging issues faster than most humans, but our documentation is... well, let's just call it "slop" for now.
1. Why We Exist
Manual triage is the ultimate "janitorial work" of open source. You wake up, check your repo, and there's a mountain of un-categorized issues. Some are bug reports, some are feature requests, and some are just... confusing.
❌ Old Model: Wait for a maintainer (usually caffeinated) to manually label and reply. ✅ New Model: Issue hits the repo -> AI analyzes the intent -> Instant categorization.
2. The Mental Model
We didn’t just want another bot that says "Thanks for the issue!" We wanted one that actually does the work. Here’s the technical flow of our triage logic:
- Source: GitHub Issue (via
github.event). - Sanitization: We truncate the body to 1500 chars. Why? To keep the context window lean and prevent prompt injection "slop."
- Inference: We use Groq’s Llama-3.1-8b-instant with a temperature of 0.5 for predictable, high-signal responses.
- Outcome: A categorized comment posted via the GitHub API.
3. Why You Should Contribute ⚡
We need your help to fix the "educational slop." Whether you're a seasoned dev or just starting out, your contributions to these docs are a high-value move.
- High Dopamine: We respond to PRs fast. No sitting in the queue for months.
- Real Impact: Your edits help other devs navigate the LLM-powered maintenance landscape.
How to Start
- Spot a Typo? Popped it? Fix it!
- Confused by a Feature? Document the logic!
- Have a Better Way? Share the fix!
Check out our GitHub Issues for good first issue labels, or just dive into the docs/ folder and start refactoring.
Let's make this project's documentation as sharp as its inference.