Debugging AI Isn’t About More GPUs — It’s About Semantic Firewalls

Most people assume scaling GPUs or adding more data will solve their AI problems. In practice, the same failure patterns repeat:

• RAG pipelines collapsing on bad chunking
• embeddings drifting into useless space
• OCR pipelines hallucinating structure that isn’t there
• fine-tunes poisoned because semantic layers weren’t separated

I’ve been tracking these issues in real-world projects (LLM infra, agent frameworks, RAG deployments) and the pattern is always the same. The infra looks fine, the code looks fine — but the semantic layer is silently collapsing.

That’s why I started building a Problem Map: a checklist of 16 common failure modes (e.g., “No.5 Semantic + Embedding drift”) and corresponding modules to intercept them. The idea is not to rebuild your infra, but to place a semantic firewall so errors don’t contaminate downstream.

The effect has been surprising. Instead of wasting weeks re-fine-tuning or rewriting code, people debug the map, flip the right fix, and their pipeline recovers in minutes.

I’ll share some case studies in the coming weeks (OCR, vector store, Bedrock throttling, etc.). For now I’m curious — have you run into a situation where everything looked fine, but the model still collapsed in subtle ways?

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