Which as far as I understand it is summarizing the context with a smaller model.
Am I misunderstanding you, as the practical experience of most people seem to contradict your results.
Once we start to see that kind of self feedback going in next iterations (w/ possible training runs between sessions, "dreaming" stage from og RL, distilling a session, grabbing key insights, storing them, surfacing them at next inference, etc) then we'll see true progress in this space.
The problem is that a lot of people work on these things in silos. The industry is much more geared towards quick returns now, having to show something now, rather than building strong fo0undations based on real data. Kind of an analogy to early linux dev. We need our own Linus, it would seem :)
You can also ask the llm at the end of a feature chat to prepare a prompt to start the next feature chat so it can determine what knowledge is important to communicate to the next feature chat.
Summarizing a chat also helps getting rid of wrong info, as you’ll often trial and error towards the right solution. You don’t want these incorrect approaches to leak into the context of the next feature chat, maybe just add the “don’t dos” into a guidelines and rules document so it will avoid it in the future.
in a similar vein, i match github project issues to md files committed to repo
essentially, the github issue content is just a link to the md file in the repo also, epics are folders with links (+ a readme that gets updated after each task)
i am very happy about it too
it's also very fast and handy to reference either from claude using @ .ie: did you consider what has been done @
other major improvements that worked for me were - DOC_INDEX.md build around the concept of "read this if you are working on X (infra, db, frontend, domain, ....)" - COMMON_TASKS.md (if you need to do X read Y, if you need to add a new frontend component read HOW_TO_ADD_A_COMPONENT.md )
common tasks tend to be increase quality when they are epxpressed in a checklist format
How do you define success of a model's run?
My point was more on tracking these signals over time. And using them to improve the client, not just the model (most model providers probably track this already).
My somewhat terse/bitter question was because yesterday Claude would continue claim to have created a "production-ready" solution which was completely entirely wrong.
I would've loved to have the feedback loop you describe
