| name | flow-skill-engineer-prompts-for-reasoning |
| description | Guide for writing prompts for reasoning/smart models (Gemini Pro, GPT-4o, Claude 3.5 Sonnet), focused on structure and context. |
HOW TO WRITE PROMPTS FOR REASONING MODELS
This guide helps you get the best out of "smart" models (like Gemini 1.5 Pro,
Claude 3.5 Sonnet, GPT-4o). These models are capable of complex logic, coding,
and creative work, but they need Context and Structure to stay on track.
1. THE CORE CONCEPT: "STRUCTURED CONTEXT"
Reasoning models thrive when you organize information clearly. Think of it like
briefing a senior colleague. You don't just give an order; you explain the
Background, the Goal, and the Constraints.
We use XML-style tags (like <context>, <rules>) to help the model
understand the structure of your prompt.
2. THE REASONING FRAMEWORK (BEGINNER TEMPLATE)
Copy this structure for complex tasks.
# ROLE
You are an expert [Role Name].
# GOAL
<objective>
[Clearly state what you want to achieve in 1-2 sentences]
</objective>
# CONTEXT (The "Why" and "What")
<context>
[Provide background info. Who is the audience? What is the current state? What are the definitions?]
</context>
# RULES & CONSTRAINTS
<rules>
1. [Constraint 1 - e.g., Code style]
2. [Constraint 2 - e.g., Word count limit]
3. [Constraint 3 - e.g., "Do not use external libraries"]
</rules>
# INSTRUCTIONS (The "How")
<instructions>
1. First, analyze the request and the context.
2. Think step-by-step about the best approach.
3. [Specific Step 1]
4. [Specific Step 2]
5. Output the final result in [Format].
</instructions>
3. KEY TECHNIQUES FOR BEGINNERS
A. Use XML Tags for Clarity
Tags like <context>, <code_snippet>, <examples> help the model separate
different parts of your prompt. It prevents the model from getting confused
between instructions and data.
B. Ask for a Plan First
For coding or writing tasks, ask the model to outline its plan or "think" before
generating the final output.
- Prompt: "Draft a plan in
<plan> tags, then write the code."
- Why: It catches misunderstandings early.
C. Define Success Criteria
Tell the model exactly what "good" looks like.
- "A successful response will cover all edge cases and pass the linter."
- "A successful response will be friendly but professional."
4. EXAMPLE: CODE REFACTORING
# ROLE
You are a Senior TypeScript Engineer.
# GOAL
<objective>
Refactor the provided legacy function to be more readable and performant.
</objective>
# CONTEXT
<context>
This function is part of a high-traffic e-commerce checkout. It handles cart validation.
We are moving to functional programming patterns.
</context>
# RULES
<rules>
1. Use arrow functions.
2. Add JSDoc comments.
3. Do not change the external API signature.
4. Return early to avoid deep nesting.
</rules>
# INPUT CODE
<code_snippet> function validate(cart) { // ... messy code ... } </code_snippet>
# INSTRUCTIONS
<instructions>
1. Analyze the complexity of the current function.
2. Refactor step-by-step.
3. Explain why the new version is better.
</instructions>
5. CHECKLIST FOR SUCCESS
Before sending your prompt, ask yourself:
- Role: Did I say who the AI is?
- Context: Did I explain why we are doing this?
- Format: Did I specify how the output should look?
- Tags: Did I use
<tags> to organize big blocks of text?
