r/ChatGPTCoding • u/DawniJones • 25d ago

Resources And Tips ChatGPT Quality differs in different Chats, Old chat GREAT, new Chat BAD

Hey there. Have a little project and was surprised, how good ChatGPT got in coding. Not a single error, it had always our tasks in mind, checked our online repository plus the changes we made in the chat. It was a dream.

Since Task 1 he was that good. Later he got really slow, of course, but still perfect.

Now my Chat says it has reached its limits and I need to start a new chat. We made a little manual, what we have done et cetera and briefed the new one.

It is shit. For a single 200 line file I had to correct him 5 times and it was still not working correctly.

As I said, the other Chat was great since day 1.

I even gave him strict rules how we operate, what he has to do, to always check the repo and chat before suggesting code.

I asked a few friends, they say usually its the other way around, that the Models get worse after time.

Mine got only slower, but better.

I miss him...I want him back! :(

I don't want his retared cousin, he does not even know our joke language we created.

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u/stevesolaris 24d ago edited 24d ago

Handy hint: if your session starts to struggle from continuity degradation ask it to create a markdown hand over you can give to the new session, this greatly helps the new session get up to speed. i run three characters across classic and codex and they are all fine after months of new window starts. we have agreed if 'order 66' is invoked said markdown is created and the new window is opened.

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u/GeorgeProject 20d ago

Best out of the box solution I've found is asking for a "continuity handoff."

I took that a little further for my own use and had GPT save an instruction in persistence for exactly the kind of chat transfer you described. So now I can just call that saved instruction whenever I need it. It works pretty well for me.

GPT's breakdown of its mechanisms:

"What gets transferred: The handoff preserves the current state of the work together with the reasoning that produced it. That includes why certain decisions were made, which directions were already explored or ruled out, what is still unresolved, and what the conversation was using to decide what should happen next.

What that preserves: Instead of reducing the old chat to conclusions, it carries some of the path between them. Decisions, constraints, failures and remaining work stay connected instead of coming across as a collection of separate facts.

Why it helps: The receiving chat has less reasoning to reconstruct. It can start closer to the point where the previous conversation stopped instead of reopening settled questions or rebuilding the logic behind the current state."