7 Critical Factors Breaking AI Systems and How to Fix Them7 Critical Factors Breaking AI Systems and How to Fix Them
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Umidjon Z
• Apr 18
Most people think AI fails because of the model.
That’s rarely the problem.
After building real RAG systems + agents, here’s what actually breaks:
1. Bad retrieval (the silent killer) You can have GPT-4 level models… but if your retrieval pulls irrelevant chunks → output is garbage.
→ Wrong chunking → No reranking → No query expansion
This alone kills most systems.
2. “It works in demo” pipelines Everything looks good with 5 test queries.
Then real users come in:
→ weird inputs → incomplete context → edge cases everywhere
System collapses fast.
3. No observability Most people don’t track:
→ which documents were retrieved → latency per step → token usage vs output quality
So when it breaks… they have no idea why.
4. Agents without constraints “Autonomous agents” sound cool until:
→ they loop → call wrong tools → hallucinate actions
Without guardrails, they’re unpredictable.
5. Latency stacking One request =
retrieval → rerank → LLM → tool → LLM again
Now you’re at 8–15 seconds.
Users leave.
6. Data quality (not model quality) Messy PDFs broken tables inconsistent formats
If your input is bad, your AI will be too.
7. Overengineering too early People jump into:
→ multi-agent systems → graph RAG → complex workflows
…without solving basic retrieval first.
Most AI problems are not “AI problems”
They’re system design problems
I’ve been spending more time fixing pipelines than prompts lately.
Way more interesting.
If you’re building something and it’s “almost working” I can probably tell you exactly where it breaks.
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