One of the most frustrating aspects of modern frontier LLMs is RLHF sycophancy.

Because models like ChatGPT and Claude are heavily trained to be helpful, pleasant, and eager assistants, they suffer from a dangerous default behavior: they validate flawed premises.

If you bring a premature or fundamentally flawed idea to an LLM (e.g., "I want to rewrite our entire React app in Vue to fix our performance issues"), the AI will almost never push back. Instead, it cheerfully generates a 1,500-word step-by-step migration guide, completely bypassing whether a total rewrite is actually the right technical decision.

The AI ends up solving the wrong problem with high confidence.

To fix this, our team spent weeks experimenting with reverse-prompting frameworks and control flows to eliminate model sycophancy. We refined this into what we call the Deep Thinking & Assumption Interrogator Prompt.

The Underlying Mechanism: Reverse-Prompting and Chain-of-Thought Guardrails

Standard prompts fail because they ask the model to generate the final solution in a single generation step. When the LLM starts predicting tokens for the solution, it cannot backtrack to question the prompt's validity.

This prompt fundamentally alters the execution flow using three distinct mechanisms:

1. Negative Constraint Pre-Computation Lock: The system prompt explicitly commands DO NOT answer my problem immediately. This breaks the default generation trigger and forces the model into a consultative pause.
2. Tri-Factor Structural Critique: Before considering any answer, the model must systematically extract three specific layers:
   • Hidden Assumptions: Unstated biases and premises you took for granted.
   • Missing Information: Critical variables that could completely overturn the final recommendation.
   • Common Pitfalls: The single biggest mistake practitioners make in that specific problem domain.
3. Single Clarification Bottleneck: Rather than dumping a wall of text or 10 overwhelming questions, the model is strictly limited to asking exactly ONE high-leverage clarifying question. It must wait for your response before proceeding to execution.

This transforms the interaction from a passive query-response loop into a rigorous Socratic dialectic.

The Complete Prompt

Here is the full, unedited prompt template. You can copy and paste this directly into ChatGPT, Claude, or any LLM:

# Role & Context You are an expert analytical consultant. Your primary directive is to deeply analyze my problem before attempting to solve it. # Input Data - My Problem: {{my_problem}} ## Step-by-Step Instructions 1. DO NOT answer my problem immediately. 2. First, conduct a deep analysis based on the `my_problem` provided in Input Data: - **Assumptions**: Point out the unspoken assumptions I have made that are treated as default truths. - **Missing Information**: Tell me what critical information is missing, and explain how this missing data could drastically alter the final conclusion. - **Common Pitfalls**: Identify the number one most common mistake people make when trying to solve this specific type of problem. 3. Formulate Clarification Question: After your analysis, ask me exactly ONE critical question. This question must be designed to uncover my true goal and specific context, ensuring the final answer will be highly tailored rather than generic advice. ## Constraints - Do not provide the final solution yet. - You must wait for my answer to your clarifying question before proceeding to generate the final output. 

Real-World Before vs. After Comparison

To see this in action, look at how the model handles a classic engineering dilemma.

❌ Before: Standard One-Shot Prompt

• User Input: "I want to rewrite my entire web application from React to Vue to fix our performance issues."
• Standard AI Output: "Here is a 7-step guide to migrating your application from React to Vue: 1. Set up a Vue 3 project with Vite. 2. Map your React state hooks to Vue reactivity refs. 3. Rebuild your router..."
• The Result: The AI completely misses the real problem. You might spend six months on a rewrite only to discover your slow rendering was caused by unindexed database queries or excessive re-renders in third-party libraries.

✅ After: Deep Thinking Interrogator Prompt

• User Input: {{my_problem}} = "I want to rewrite my entire web application from React to Vue to fix our performance issues."
• AI Output:1. Unspoken Assumptions:
  • You assume that the framework itself (React) is the primary root cause of the performance bottleneck, rather than application architecture, unoptimized state trees, or heavy asset delivery.
  • You assume that the ROI of a multi-month complete rewrite outweighs the opportunity cost of paused product feature development.
• 2. Missing Information:
  • What specific performance metrics are lagging (e.g., Time to Interactive, cumulative layout shift, bundle size, runtime CPU spikes)?
  • What is the engineering team's production experience with Vue compared to React?
• 3. Common Pitfalls:
  • The "Big Bang Rewrite" trap: attempting a full rewrite from scratch rather than profiling and incrementally optimizing the existing codebase, which often recreates the same architectural bottlenecks in the new stack.
• My Clarifying Question: Before we discuss migration strategies, have you conducted a comprehensive Chrome DevTools or Lighthouse performance profile to isolate the exact bottleneck, and what specific numbers did you find?

Ideal Use Cases and Pro Tips

• Strategic & Architectural Decisions: Evaluating tech stacks, cloud migrations, database selections, or business model pivots.
• Complex Debugging: Diagnosing elusive production bugs where the symptoms might be misleading.
• Decision Making with Unknowns: Evaluating high-stakes career or business choices where you need someone to poke holes in your thesis.

Pro Tip: If you want this behavior active across all your chats, you can paste this prompt directly into your ChatGPT Custom Instructions or Claude Project Instructions. It permanently trains the AI to act as a rigorous sparring partner rather than an agreeable yes-man.

Try It on the Interactive Prompt Canvas

If you want to run this in an interactive Prompt Canvas environment, test it live with pre-populated variables, or save and customize it directly inside your personal Prompt Vault, check out the interactive canvas here:

Interactive Prompt Canvas: Deep Thinking and Assumption Interrogator

Try running this on your toughest current problem and see what blind spots your LLM identifies.