By skyforbes Why do many prompts lose accuracy over time, even when nothing is changed?
I’m not talking about lazy prompts.
I mean fully structured, layered prompts that start strong…
but by the 5th–10th run, the output becomes weaker, inconsistent, or off-track.
Over the past 2 weeks I tested:
• GPT-3.5 / GPT-4 / Claude 3 / Gemini
• Fresh chat vs continued thread
• Freestyle prompts vs layered system prompts
Same result every time:
✅ New chat = accuracy restored
❌ Same thread = output slowly drifts
I was going to release a free version of one of my prompt systems today,
but I decided to pause — because shipping a drifting system helps nobody.
So here’s the question:
Q: What do you think causes this drift?
A) Model state contamination
B) Hidden memory / bias accumulation
C) Prompt structure fatigue
D) Context-window residue
E) Something else?
Once the discussion settles, I’ll follow up with:
✅ Drift test log summary (publishing soon)
✅ The “anti-drift” prompt architecture
✅ A stable .txt demo file (free download)
Let’s solve the drift problem before we ship unreliable tools.