[msejas]

I approach this by always asking Opus to send an agent to explore and trace how a pipeline works. Even better if I have an integration test. Once it's fully mapped out I might ask it to dump everything it discovered on a markdown doc, clear the context and start the task. The docs folder keeps the information intact for future development.

Managing context is by far the most important skill to be effective with LLMS, in addition to having already existing clean code on the codebase.

As they read your files, you are one shot training the LLM in how to write code and how you structure it and it will adapt. With clean codebases, I found the LLMs were outputting well documented, well logged, and even tested functions by default because the other files it interacted with were like this, 'it learns'.

Additionally you have to think how they train and evaluate the model, there are so many use cases to cover, I'm pretty sure in the Reinforcement Learning part they are not going in huge long threads, but are actually benchmarking and optimizing from fresh context starts, and you should do that as much as possible in your tasks.

[sshadmand]

I have a similar flow, but do you connect that back to roadmap, tickets, or workflow for posterity or tracking?

Also, some say they use the tickets themselves for the prompt and have Claude CLI (or alike) just work off the tickets directly.