The whole first half of the track taught you to sustain a conversation: insist, correct, reaffirm. That works to a point. Past that point, the conversation is contaminated and the best move is to throw it out.
The criterion for detecting the reset moment is practical, not philosophical.
You repeated the same correction 3+ times. If you had to say three times "I don't want JSON, I want YAML", the model is not going to start understanding on the fourth try. previous context is outweighing you.
The model assumes things you explicitly denied. Sign that the weight of "the user probably wants X" beats the weight of "the user said NOT X".
You lost track of state. If you can't sum up in 3 lines what was agreed, the model can't either.
It's not throwing everything out. It's distilling what you learned into a clean prompt:
I want <final goal>.
I do NOT want: <discard 1>, <discard 2>, <discard 3>.
I need: <concrete spec>.
Question: <one clear question>.That recipe loads the useful context of the 10 old turns into 4 lines, without dragging the misunderstandings.
If you reset every time the model drifts a single turn, you lose the incremental learning. The rule isn't "first doubt, reset". It is:
| Situation | Action |
|---|---|
| First drift | Specific follow-up (lessons 1-9). |
| Second drift of the same thing | Reaffirm with emphasis (lessons 11-12). |
| Third drift of the same thing | Summarize and restart conversation. |
This is the opposite of the beginner instinct, which is "I already invested 10 turns, it'd be a waste to throw them out". That's sunk-cost fallacy. the 10 turns are already gone. The only thing that decides future cost is how clean you start the next turn.
On the right, the decision moment: keep going or restart?