Your LlamaIndex app runs. Your answers are still wrong.

[onestardao]

Your LlamaIndex app runs. Your answers are still wrong.

I keep seeing the same pattern with LlamaIndex projects:

The app is up.
The documents are indexed.
The retriever returns something.
The query engine runs.
The chat flow looks fine.

But the final answer is still off-topic, unstable, or just wrong.

That is exactly the gap this post is about.

LlamaIndex is great at helping builders connect data, indexes, retrievers, query engines, and chat interfaces into a real RAG application.
But once the app is already running, a different problem starts:

How do you debug the part that still breaks?

A lot of production failures look the same from the outside.
Users just say “the answer feels weird.”
But the real cause might be very different:

Maybe retrieval found the wrong chunk.
Maybe retrieval found the right chunk, but the final answer still drifted.
Maybe the chat flow works locally, then becomes inconsistent in production.
Maybe the pipeline is technically “successful,” but the response quality is still unreliable.

That is why I built the WFGY RAG 16 Problem Map · Global Debug Card.

This is not a replacement for LlamaIndex.
It is a lightweight debug layer for the failures that show up after your LlamaIndex app is already running.

The idea is simple:

Save one image.
When you get one real failing run, upload that image plus the failing run to any strong LLM.
Then ask the model to follow the card and tell you:

• what likely went wrong
• which failure modes fit best
• what concrete fixes to try next

So instead of staring at a “working” pipeline and guessing in the dark, you get a structured way to turn one broken run into a more useful debug path.

I made this because I wanted something practical for real RAG incidents, especially the annoying ones where everything looks normal in the stack, but the answer quality is still clearly broken.

I have already tested this card with:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Grok

All of them can read the card and use it to identify common RAG failure patterns and suggest reasonable fixes.

For LlamaIndex users, I think this is most useful when you hit problems like:

• retrieval looks fine, but the answer still hallucinates
• the same question gets different quality across runs
• production Q&A drifts even though the pipeline “works”
• your team knows something is wrong, but does not have a simple shared language for the failure yet

If that sounds familiar, try the card on one real broken run.

I’m dropping the card image below.
HD version + README are here:

https://github.com/onestardao/WFGY/blob/main/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md