[Docs] Proposal for a practical deep-search / retrieval debugging companion guide

[onestardao]

Hi, I’d like to suggest a practical companion resource that may be helpful for users dealing with deep-search and retrieval-heavy workflows.

The resource is the WFGY RAG 16 Problem Map, a framework-agnostic troubleshooting checklist focused on common downstream failure modes in LLM and retrieval pipelines.

Reference:
https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md

It focuses on the kinds of issues that often appear after the search system technically “works,” but results still feel unreliable, such as:

• relevant material not being surfaced
• stale or low-quality evidence dominating retrieval
• chunking and embedding mismatch
• ranking instability
• context overload
• grounded evidence present, but final answer still drifts
• multi-step reasoning becoming inconsistent

I think this may be useful as a practical companion reference because users often need a structured way to reason about “why a search-backed answer failed,” not just how to run the system.

This Problem Map has already been referenced or integrated by several public RAG / LLM ecosystem projects, including:

• RAGFlow
• LlamaIndex
• ToolUniverse (Harvard MIMS Lab)
• Rankify
• Multimodal RAG Survey (QCRI LLM Lab)

If this sounds useful, I’d be happy to open a small docs-only PR with concise wording and repo-style scope.

Thanks for your time.