Open-source playbook for RAG and LLM debugging: Problem Map 2.0 and Semantic Clinic

#webdev #rag #ai #tutorial

Large-scale language systems tend to break in repeatable ways: retrieval drift, interpretation collapse, hallucinated checkpoints.
We’ve open-sourced both the patterns and the fixes, so you can ship models that actually stay on track.

What’s inside:

Problem Map 2.0
A structured collection of 16 real-world failure modes found in retrieval-augmented generation, multi-agent loops, and long-chain reasoning pipelines.
Link: https://github.com/onestardao/WFGY/tree/main/ProblemMap

Semantic Clinic Index
A one-page triage table. Just identify the symptom, then jump directly to the correct fix.

Link: https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md

Zero-install Colab demos
Test semantic drift, domain misalignment, diversity collapse, and more—directly from browser.
(Colab links are included on each solution page.)

All resources are MIT-licensed and derived from actual production debugging, not synthetic benchmarks.

How to use it:

Describe the failure (for example: "The vector store finds the correct chunk, but the answer is wrong").

Open the Semantic Clinic and locate the matching symptom.

Follow the link to Problem Map 2.0. Each page includes: root cause, step-by-step fix, and testable patch logic.

If you're short on time, open a Colab sandbox and paste your prompt and answer. You'll get instant drift metrics. No keys or setup required.

Why this project matters:

Most LLM bugs are not random—they follow consistent patterns.
Problem Map 2.0 gives you a diagnostic catalog instead of random Slack threads.

Traditional debuggers only show token-level output.
This toolset maps the full semantic path: prompt to retrieve to reason to deploy.

It’s open access and community-ready.
Everything is on GitHub. You can fork, modify, or integrate it into your own pipelines.

Upcoming:

CLI tools to detect entropy collapse and multi-agent override issues
Public release of the Drunk Transformer safety layer (WRI, WAI, WDT, WTF)
Real-world case studies and pull request showcases

If this toolkit saves you debug time, feel free to star the repo or share your edge case.

Suggested keywords: RAG debugging, LLM failure modes, semantic drift detection, open-source reasoning tools.