prompt-improver

Prompt Improver

Improve prompts using best practices for clarity, specificity, and structure. Includes simplification patterns for reducing token waste.

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Core Principles

1. Be specific — vague prompts get vague answers
2. Provide context — include relevant constraints and examples
3. Define output format — tell the model how to respond
4. Use structure — headings, lists, code blocks
5. Set constraints — what NOT to do is as important as what TO do

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Improvement Patterns

Before → After

Vague:

Make this code better

Specific:

Refactor this function to:
1. Use async/await for all I/O
2. Add type hints to parameters and return
3. Extract the validation logic into a separate function
4. Keep the same behavior (tests must pass)

Wordy:

I need you to please help me understand what this code does because I'm not sure and it's kind of confusing and there are a lot of parts and I don't know where to start looking at it

Concise:

Explain what this code does. Summarize in 3 bullet points.

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Prompt Structure Template

# Context
[What is the background? What problem are we solving?]

# Task
[What exactly should be done? Be specific.]

# Constraints
- [Constraint 1: e.g., "Use only existing libraries"]
- [Constraint 2: e.g., "Do not change the API"]

# Output Format
[How should the response be structured?]

# Examples (optional)
[Show expected input/output if helpful]

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Simplification Rules

• Remove filler words: "please", "I think", "maybe", "kind of"
• Use active voice: "Write tests" not "Could you write some tests?"
• One task per prompt (or numbered list for multiple)
• Use code blocks for code references
• Set explicit limits: "Max 3 suggestions", "Under 100 words"

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Anti-Patterns

• ❌ "Do your best" — model doesn't know what "best" means
• ❌ Multiple conflicting instructions in one prompt
• ❌ No output format specified
• ❌ Assuming model knows project context
• ❌ Overly long prompts with irrelevant details