I've had experiences like this before but if that's the ONLY experience you've had, or if you have that experience 75% of the way, I think you're doing something wrong. Or perhaps you're just in a very different coding domain than I am (web dev, HTML/CSS/JS) where the AI happens to suck.
The biggest mistakes imo are:
1. Underplanning. Trying to do huge projects in one go, rather than breaking them down into small projects, and breaking those small projects down into well thought out plans.
2. Too much of a focus on prompting rather than context. Prompt engineering is obsessing with the perfect way to say or phrase something. Whereas context engineering putting relevant information into the LLM's working memory, which requires you to go out and gather that info (or use the LLM to get it).
I've had my share of good and bad experiences, one section of an existing project more than 90% ai created. How you say things is equally important to the context you provide, partly because the agents will start trying to decide what is and is not good context, which they are unreliable in doing, even after you give them the limited context and tell them not to edit other files or bring in more context. For example, if you use a lot of colloquial phrases, you activate that area of the network, taking away from using other parts (MoE activation, also lower level too)
They are not good readers (see research results around context collapse and context poisoning)
The biggest mistakes imo are:
1. Underplanning. Trying to do huge projects in one go, rather than breaking them down into small projects, and breaking those small projects down into well thought out plans.
2. Too much of a focus on prompting rather than context. Prompt engineering is obsessing with the perfect way to say or phrase something. Whereas context engineering putting relevant information into the LLM's working memory, which requires you to go out and gather that info (or use the LLM to get it).