TLDR:
Forget “secret prompts.” Real prompt engineering is about building meta-cognitive feedback loops inside the model’s decision process — not hacking word order.
Here’s how I just trained GPT-5 to self-correct a perceptual bias in real time.

🧠 The Experiment

I showed GPT-5 a French 2€ coin.
It misidentified the design as a cannabis leaf – a classic pattern recognition bias.
Instead of accepting the answer, I challenged it to explain why the error occurred.

The model then performed a full internal audit:

• Recognized anchoring (jumping to a plausible pattern too early)
• Identified confirmation bias in its probabilistic ranking
• Reconstructed its own decision pipeline (visual → heuristic → narrative)
• Proposed a new verification sequence: hypothesis → disconfirmation → evidence weighting

That’s not “hallucination correction.”
That’s cognitive behavior modification.

⚙️ The Breakthrough

We defined a two-mode architecture you can control at the prompt level:

Mode	Function	Use Case
EFF (Efficiency Mode)	Prioritizes speed, fluency, and conversational relevance	Brainstorming, creative flow, real-time ideation
EVD (Evidence Mode)	Prioritizes verification, multi-angle reasoning, explicit uncertainty	Technical analysis, decision logic, psychological interpretation
MIX	Starts efficient, switches to evidence mode if inconsistency is detected	Ideal for interactive, exploratory work

You can trigger it simply by prefacing prompts with:

Mode: EFF → quick plausible response  
Mode: EVD → verify before concluding  
Mode: MIX → adaptive transition

The model learns to dynamically self-correct and adjust its cognitive depth based on user feedback — a live training loop.

🔍 Why This Matters

This is real prompt engineering —
not memorizing phrasing tricks, but managing cognition.

It’s about:

• Controlling how the model thinks, not just what it says
• Creating meta-prompts that shape reasoning architecture
• Building feedback-induced re-calibration into dialogue

If you’re designing prompts for research, automation, or long-form cognitive collaboration — this is the layer that actually matters.

💬 Example in Context

That’s not a correction — that’s a trained cognitive upgrade.

🧩 Takeaway

Prompt engineering ≠ tricking the model.
It’s structuring the conversation so the model learns from you.