flow-skill-engineer-prompts-for-reasoning

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:

1. Role: Did I say who the AI is?
2. Context: Did I explain why we are doing this?
3. Format: Did I specify how the output should look?
4. Tags: Did I use <tags> to organize big blocks of text?