After weeks of collaborating with ChatGPT as my engineering partner while building an AI platform, I started noticing recurring collaboration patterns. They weren't random. They appeared again and again, often with surprisingly predictable triggers—and equally predictable fixes.
When I started this project, I expected the biggest challenges to be Python, APIs, embeddings, vector databases, or understanding RAG. I was wrong. So far, the hardest part hasn't been building the platform. It's been learning how to work effectively with ChatGPT as an engineering partner over the course of a long-term project. Eventually, I realized I was encountering the same failure modes over and over again. Once I recognized them, I could often predict when they would happen—and, more importantly, how to avoid them.
This article is based on my experience with ChatGPT. Other LLMs—such as Claude, Gemini, or open-weight models—may exhibit some of these behaviors differently, or not at all. Still, I suspect many of the underlying collaboration challenges are shared across today's generation of LLMs.
Here are the ten failure modes I encountered most often:
Symptom
