The quality of AI-assisted software depends on unit of work management

I found out that summarizing a completed task and feeding it to a new context works better than staying on the same context for multiple tasks. So let’s say I have a sprint with tasks 1, 2 and 3. I start by creating a project with general information including the spec, git issues, code base, folder trees, etc then work on Task 1. When done I ask for a summary using a template, which gives me a txt file describing what the original goal was, what we changed and what the next steps are. Then I repeat the process for Task 2 and I feed the summary from Task 1. At least in ChatGPT keeping the same context for multiple tasks has lots of issues like speed, increased hallucinations, and ChatGPT referencing content from old files.

Hell, claude even makes that part of the standard workflow, with /compact; cleverly using the llm itself to summarize the previous context