Three Experts Method
The Three Experts prompt is a powerful reasoning pattern designed to help AI simulate deeper thinking and avoid shallow or generic responses. In agentic coding, it’s especially useful when you’re working through complex architectural decisions, refactoring strategies, or debugging multi-layer issues.
This method turns the AI from a code generator into a panel of thought partners, each exploring the problem from a different angle — and converging on the most reasoned solution.
What’s the Method?
Core Prompt Pattern:
Simulate three different experts answering the question below.
Each expert will write down one step of their thinking and share it with the group.
Then all experts move to the next step.
If any expert realizes they're wrong at any point, they drop out.
Continue until there’s consensus on the final answer.
This creates a multi-step reasoning loop that encourages internal critique and deeper analysis.
Use Case | Why It Works |
---|---|
Choosing between architectural strategies | Reveals tradeoffs in tech decisions |
Refactoring complex modules | Surfaces alternative paths + risks |
Debugging non-obvious bugs | Simulates root-cause analysis |
Designing data models or APIs | Balances structure, performance, and clarity |
Generating edge-case tests | Experts act like QA engineers or systems thinkers |
Example 1: Refactoring a Cross-Cutting Module
Goal: Decide how to refactor a shared NotificationService used across microservices.
Prompt:
Simulate three backend experts reviewing how to refactor the NotificationService, which is currently tightly coupled to both billing and auth services.
Each expert will write one step of reasoning at a time and discuss with the group.
If one is clearly wrong, they should drop out.
Keep going until they converge on a refactoring strategy that isolates the service and makes it reusable.
Expected Outcome:
- Tradeoffs discussed (e.g., pub/sub vs abstraction layer)
- Consensus reached through elimination of flawed approaches
- Final recommendation is reasoned, not guessed
Example 2: Debugging a Non-Obvious UI State Bug
Goal: The UI shows stale data after login. Why?
Prompt:
Simulate three frontend engineers exploring why the dashboard shows stale user data after login.
Each one will reason step-by-step based on their own assumption:
Expert A suspects caching issues
Expert B suspects async state update problems
Expert C suspects token propagation failure
They’ll reason in steps and eliminate theories as they go.
Stop when they reach the most likely root cause.
Expected Outcome:
- Stepwise root-cause analysis
- Use of logs, network traces, or event timing
- Stronger hypothesis to guide debugging prompt
Example 3: Designing a Robust Validation System
Goal: Choose between Zod, Yup, or class-validator for a multi-form web app.
Prompt:
Simulate three full-stack engineers debating which validation system to use for a multi-form onboarding flow with dynamic field types.
Each expert will write one reasoning step at a time.
Expert A prefers Zod, B prefers Yup, C prefers class-validator.
If any realize their approach won't scale or violates team standards, they should drop out.
Stop once consensus is reached.
Expected Outcome:
- Clarity on tradeoffs (TypeScript friendliness, async rules, nesting support)
- Selection based on real needs (team standards, backend integration)
Summary: Why Use the Three Experts Pattern?
Benefit | Why It Helps in Agentic Coding |
---|---|
Deepens reasoning | Prevents superficial or default answers |
Simulates tradeoffs | Mirrors real-world team decision dynamics |
Reduces hallucination | Experts challenge each other’s logic |
Enables exploration before code | Supports “reason first, code later” mindset |
Works well with Claude, GPT-4o, Cursor Agent Mode | High-context tools thrive on multi-step prompts |
Bonus Variation
For complex decisions:
Simulate a system architect, a QA lead, and a product manager reasoning together.
Each shares their perspective, step-by-step, to align on a final implementation plan.