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Lesson 02 of 05 · published

The Re-Prompt Spiral

~22 min · loop, rot, anti-pattern

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Repeated agreement, repeated violation

If the model keeps violating the same rule after correction, you are in a re-prompt spiral. The classic shape: human corrects, model agrees, model violates again, human corrects more emphatically, model agrees more enthusiastically, model violates again. Each cycle adds tokens but does not change behavior.

Why it happens

Old context is still in the window, still influencing attention. Adding more reminders amplifies noise without removing the original misdirection. The session has lost the attention battle, and more prompting is the wrong tool.

Break the spiral

Stop adding reminders. Capture the live truth in a durable note. Start a fresh session. Reload the note and current source. Continue from a clean prefix. The fresh session usually behaves correctly within one turn — proof that the problem was context pollution, not capability.

When a session is polluted, more prompting can be the problem, not the cure.

Code

Spiral detector·python
def in_reprompt_spiral(history, rule_text):
    violations = sum(1 for turn in history[-10:] if rule_text in turn.violation_log)
    corrections = sum(1 for turn in history[-10:] if rule_text in turn.correction_log)
    return violations >= 3 and corrections >= 3

if in_reprompt_spiral(session.history, "do not push"):
    plan_recovery_to_fresh_session()
Recovery plan·yaml
recovery_plan:
  capture:
    - live rules with source
    - current state
    - failed attempts worth remembering (if any)
  reset:
    - fresh session OR explicit compaction with audit
  reload:
    - durable note
    - current source files
    - latest test/git state
  continue:
    - exactly one next safe action

External links

Exercise

Describe a re-prompting spiral you have seen (real or imagined). Write the recovery plan that should replace it: capture, reset, reload, continue.
Hint
Look for repeated agreement followed by repeated violation.

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