[montfort]

This is a common experience for everyone. A combination of small models with overly abbreviated prompts will produce poor and faulty output. This is where what's called "prompt engineering" comes in. With practice, you'll see that detailed, well-structured, and regularly large requests will yield better results. You'll also find that you can establish dialogues with the agents to refine ideas and ask them to take structured notes before starting implementation work. The idea of achieving good results with a lot of agent work stemming from a small prompt should be dismissed. It requires a lot of conversation, planning, and design on our part. Don't give up; it's just a matter of getting used to this "language" for communicating with the agents.