Prompt Library

A prompt library is a curated collection of effective prompts that you save and reuse for common development tasks. Building a prompt library eliminates the need to craft prompts from scratch each time, ensures consistency in AI interactions, and captures what works for your specific workflow.

Example

Your prompt library includes: 'Code review prompt', 'Generate tests for function', 'Explain this code to a junior developer', 'Refactor for performance', 'Add TypeScript types' — each refined through use.

A prompt library is one of the highest-leverage investments in vibe coding. Once you find prompts that work, saving them creates compounding returns.

What to Include in Your Library

Code generation prompts:

  • Component scaffolding
  • API endpoint creation
  • Test generation
  • Documentation writing

Analysis prompts:

  • Code review
  • Security audit
  • Performance analysis
  • Dependency review

Transformation prompts:

  • Refactoring patterns
  • Type additions
  • Style migrations
  • API updates

Building Your Library

Start with Pain Points

Track prompts you write repeatedly. Those are library candidates.

Refine Through Use

First version rarely optimal. Iterate based on results.

Include Context Slots

Use placeholders for variable parts:

"Review this [LANGUAGE] code for [FOCUS_AREA]. 
Consider: [SPECIFIC_CONCERNS]"

Organize by Task Type

Categories make prompts findable when needed.

Prompt Library Formats

Simple: Text file or notes app Structured: Spreadsheet with categories, prompts, notes Integrated: Cursor snippets, IDE shortcuts

Example Library Entries

TaskPrompt
Code review"Review this code for bugs, security issues, and performance problems. List each issue with severity and suggested fix."
Add types"Add TypeScript types to this JavaScript code. Use strict types, avoid 'any'. Create interfaces for complex objects."
Explain code"Explain what this code does in plain language. Then explain how it does it, step by step."

Sharing and Team Libraries

Team prompt libraries ensure consistent AI interactions across developers and capture collective learning about what works.