Back to blog
promptsproductivitytemplatesoptimization

AI Prompt Templates That Save 50% of Your Usage Quota

Copy-paste prompt templates for code review, debugging, documentation, and more. Before/after examples show how better prompts use fewer tokens and get better results.

QuotaMeter Team

AI Prompt Templates That Save 50% of Your Usage Quota

The difference between a good prompt and a bad prompt isn't just better output—it's 50-70% fewer tokens used.

That means fewer messages, longer until you hit limits, and more value from your Claude Pro, ChatGPT Plus, or Cursor subscription.

I've spent the last year refining prompt templates for common developer tasks. These are battle-tested prompts that get better results with less back-and-forth. Copy them, adapt them, save your quota.

Why Prompt Structure Matters for Quota

Every token counts against your limit—both your input AND the AI's output. Inefficient prompts waste quota in two ways:

  1. Unnecessary input tokens: Wordy prompts with redundant context
  2. Excessive output tokens: Vague prompts that produce rambling responses

Here's the math: A typical 5-message back-and-forth might use 8,000 tokens. The same task with a good prompt? 2,500 tokens in one exchange. That's 70% savings—tripling your effective quota.

If you want to understand exactly how different AI tools measure and limit your usage, check out our deep dive into AI rate limits.

The Master Template Structure

Before diving into specific templates, here's the structure that makes prompts efficient:

[ROLE/CONTEXT] - One line setting expectations
[TASK] - Clear, specific action required
[CONSTRAINTS] - What to include/exclude
[FORMAT] - Exact output structure wanted
[INPUT] - The actual code/content to work with

This structure eliminates:

  • Follow-up clarifications ("What format do you want?")
  • Unnecessary explanations ("Let me explain the concept first...")
  • Rambling responses (the format constraint focuses output)

This approach aligns with best practices from both Anthropic's prompt engineering guide and OpenAI's prompt engineering documentation.

Now let's apply this to real developer tasks.

Template 1: Code Review

The Inefficient Way (4-5 messages)

User: Can you review this code?
AI: Sure! I'd be happy to review your code. Please share it.
User: [pastes code]
AI: [Long review covering everything]
User: Can you focus on security issues?
AI: [Another long response]
User: What about performance?
AI: [Yet another response]

Cost: ~6,000 tokens, 5 messages

The Efficient Template

Review this [language] code for a [component type].

Focus on (ranked by priority):
1. Security vulnerabilities
2. Performance issues  
3. Code clarity/maintainability

For each issue:
- Line number
- Problem (one sentence)
- Fix (code snippet)

Skip: Style nitpicks, minor naming suggestions

```[language]
[paste code here]


**Cost:** ~1,800 tokens, 1 message

### Copy-Paste Version (JavaScript/TypeScript)

Review this TypeScript code for an API endpoint handler.

Focus on (ranked by priority):

  1. Security vulnerabilities (injection, auth, validation)
  2. Error handling gaps
  3. Performance issues
  4. Type safety improvements

For each issue:

  • Line number
  • Problem (one sentence)
  • Fix (show the corrected code)

Skip: Formatting, variable naming style, comments

[YOUR CODE HERE]


---

## Template 2: Debugging

### The Inefficient Way

User: My code isn't working AI: I'd be happy to help! What seems to be the problem? User: It throws an error AI: What's the error message? User: [error message] AI: Can you show me the code? User: [code] AI: What are you trying to accomplish?


**Cost:** ~5,000 tokens, 6 messages, still not solved

### The Efficient Template

Debug this [language] code.

Error: [exact error message] Expected: [what should happen] Actual: [what happens instead]

Environment: [runtime/version]

[paste problematic code]

Give me:

  1. Root cause (one sentence)
  2. Fixed code
  3. Explanation of why this fixes it (2-3 sentences max)

**Cost:** ~1,500 tokens, 1 message, usually solved

### Copy-Paste Version

Debug this JavaScript code.

Error: TypeError: Cannot read property 'map' of undefined Expected: Renders list of users from API Actual: Crashes on initial load

Environment: React 18, Node 20

[YOUR CODE HERE]

Give me:

  1. Root cause (one sentence)
  2. Fixed code
  3. Brief explanation (why this fixes it)

---

## Template 3: Writing Documentation

### The Inefficient Way

User: Can you write documentation for this function? AI: [writes very generic docs] User: Can you include examples? AI: [adds examples] User: Can you make it more concise? AI: [rewrites] User: Actually can you follow JSDoc format? AI: [rewrites again]


**Cost:** ~8,000 tokens, 5 messages

### The Efficient Template

Write [format] documentation for this function.

Include:

  • Description (1-2 sentences)
  • Parameters with types
  • Return value with type
  • One usage example
  • Edge cases/throws

Style: [concise/detailed], [formal/casual]

[paste function]


**Cost:** ~1,200 tokens, 1 message

### Copy-Paste Version (JSDoc)

Write JSDoc documentation for this function.

Include:

  • @description (1-2 sentences)
  • @param for each parameter (with types)
  • @returns (with type)
  • @example (one realistic usage)
  • @throws (if applicable)

Style: Concise, professional

[YOUR FUNCTION HERE]


### Copy-Paste Version (Python Docstring)

Write Google-style docstring for this function.

Include:

  • Summary line
  • Args section with types
  • Returns section with type
  • One Example
  • Raises section (if applicable)
[YOUR FUNCTION HERE]


---

## Template 4: Writing Tests

### The Inefficient Way

User: Write tests for this function AI: [writes one test] User: Can you add more edge cases? AI: [adds a few more] User: What about error cases? AI: [adds error tests] User: Can you use Jest? AI: [rewrites for Jest]


**Cost:** ~7,000 tokens

### The Efficient Template

Write [framework] tests for this [language] function.

Test categories needed:

  1. Happy path (2-3 cases)
  2. Edge cases (empty input, boundaries)
  3. Error cases (invalid input, throws)

Style guide:

  • Descriptive test names using "should..."
  • One assertion per test
  • No mocking unless essential
[paste function]


**Cost:** ~2,000 tokens, 1 message, comprehensive tests

### Copy-Paste Version (Jest)

Write Jest tests for this TypeScript function.

Test categories:

  1. Happy path (2-3 typical inputs)
  2. Edge cases (empty arrays, null, boundaries)
  3. Error cases (invalid types, missing required fields)

Style:

  • describe() for grouping
  • it('should...') for test names
  • One expect() per test
  • No mocking unless essential
[YOUR FUNCTION HERE]


### Copy-Paste Version (pytest)

Write pytest tests for this Python function.

Test categories:

  1. Happy path (2-3 typical inputs)
  2. Edge cases (empty, None, boundaries)
  3. Error cases (with pytest.raises)

Style:

  • test_ prefix for functions
  • Descriptive names (test_returns_empty_list_when_input_is_none)
  • One assert per test
  • Use parametrize for multiple inputs
[YOUR FUNCTION HERE]


---

## Template 5: Refactoring

### The Inefficient Way

User: Can you refactor this code? AI: [massive rewrite you didn't ask for] User: I just wanted to extract some functions AI: [another rewrite] User: Keep the same logic please AI: [third attempt]


**Cost:** ~10,000 tokens, lots of frustration

### The Efficient Template

Refactor this [language] code.

Goal: [specific goal, e.g., "extract repeated logic into functions"]

Constraints:

  • Keep the same external behavior
  • Don't change [specific things to preserve]
  • Prioritize [readability/performance/etc.]
[paste code]

Return:

  1. Refactored code
  2. Summary of changes (bullet list, 3-5 items)

**Cost:** ~2,500 tokens, targeted changes

### Copy-Paste Version

Refactor this TypeScript code.

Goal: Extract repeated validation logic into reusable functions

Constraints:

  • Keep the same API/function signatures
  • Don't change error messages
  • Prioritize readability over cleverness
[YOUR CODE HERE]

Return:

  1. Refactored code
  2. Summary of changes (bullet list)

---

## Template 6: API Design

### The Efficient Template

Design a REST API for [feature/resource].

Context: [brief app description]

I need:

  • Endpoints (method, path, purpose)
  • Request/response shapes (TypeScript types)
  • Error responses
  • One example request/response per endpoint

Constraints:

  • RESTful conventions
  • [auth method] for protected routes
  • [pagination style] for lists

### Copy-Paste Version

Design a REST API for user profile management.

Context: SaaS app where users can update their profile, upload avatar, change password.

I need:

  • Endpoints (method, path, brief purpose)
  • TypeScript types for request/response bodies
  • Error response format
  • One curl example per endpoint

Constraints:

  • RESTful conventions
  • JWT Bearer auth for all routes
  • Cursor pagination for any lists
  • Consistent error shape: { error: string, code: string }

---

## Template 7: Explaining Code

### The Efficient Template

Explain what this [language] code does.

Audience: [junior dev / senior dev / non-technical]

Cover:

  1. Purpose (one sentence)
  2. How it works (step by step, [brief/detailed])
  3. Key concepts used
  4. Potential gotchas
[paste code]


### Copy-Paste Version

Explain what this TypeScript code does.

Audience: Junior developer learning React

Cover:

  1. Purpose (one sentence)
  2. Step-by-step walkthrough (brief, not line-by-line)
  3. React concepts used (hooks, patterns)
  4. Common mistakes to avoid
[YOUR CODE HERE]


---

## Template 8: Converting Code

### The Efficient Template

Convert this [source language] code to [target language].

Requirements:

  • Idiomatic [target language] style
  • Equivalent functionality
  • [specific libraries/frameworks to use]

Don't:

  • Add features not in the original
  • Change the algorithm unless necessary
[paste code]


### Copy-Paste Version (JS → Python)

Convert this JavaScript code to Python.

Requirements:

  • Pythonic style (snake_case, list comprehensions where natural)
  • Use type hints
  • Use requests library for HTTP
  • Keep the same function signatures
[YOUR CODE HERE]


---

## Template 9: Git Commit Messages

### The Efficient Template

Write a commit message for this diff.

Format: [conventional commits / standard / custom]

[paste git diff]

Return only the commit message, no explanation.


### Copy-Paste Version

Write a commit message for this diff.

Format: Conventional Commits

  • type(scope): description
  • body if needed (what and why, not how)
[YOUR GIT DIFF HERE]

Return only the commit message.


---

## Template 10: Generating Type Definitions

### The Efficient Template

Generate TypeScript types for this [API response / data structure].

Requirements:

  • Strict types (no any)
  • Optional fields where appropriate
  • JSDoc comments for non-obvious fields
  • Export all types
[paste JSON example]


### Copy-Paste Version

Generate TypeScript types from this API response.

Requirements:

  • No any types
  • Mark nullable fields with | null
  • Use optional (?) for fields that may be missing
  • Add brief JSDoc for non-obvious fields
  • Name the main type: ApiResponse
[YOUR JSON HERE]


---

## Before/After: Real Token Savings

Let's see the actual token difference on a code review task:

### Before (Bad Prompt)
**Input:**
> hey can you look at my code and tell me if there are any problems with it? I'm not sure if it's good or not. Here's the code: [500 lines of code]

**What happens:**
- AI asks clarifying questions (waste)
- AI gives exhaustive review covering everything (waste)
- You ask follow-ups to focus on what matters (waste)

**Total:** ~8,000 tokens across 4-5 messages

### After (Template)
**Input:**
> Review this TypeScript API handler.
> Focus on: 1) Security 2) Error handling 3) Performance
> For each issue: Line number, problem, fix (code)
> Skip: Style, naming
> [500 lines of code]

**What happens:**
- AI gives exactly what you asked for
- Focused, actionable output
- No follow-ups needed

**Total:** ~2,200 tokens in 1 message

**Savings:** 72%

That's not cherry-picked—this is the typical improvement from using structured prompts.

## Building Your Own Templates

These templates follow a pattern you can apply to any task:

1. **State the task directly** — No pleasantries, no "could you please"
2. **Provide essential context** — Language, framework, environment
3. **Specify what you want** — Be explicit about deliverables
4. **Constrain the output** — What to include/exclude, format
5. **Include the input** — Code, error messages, examples

The time spent crafting a good prompt (30 seconds) saves you 5-10 minutes of back-and-forth AND preserves your quota for more important work.

For more prompting techniques, Anthropic has an excellent [interactive prompt engineering tutorial](https://github.com/anthropics/prompt-eng-interactive-tutorial) and OpenAI offers [detailed best practices](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api).

## Save These Templates

You can:
- Bookmark this page
- Copy templates to a note app
- Create a Claude Project with these as instructions
- Set up text expansion shortcuts

**Pro tip:** Create a Claude Project called "Dev Templates" and paste your most-used templates in the project instructions. They'll be available without re-typing and won't count against your per-message quota (cached context).

## Which Tool Works Best With These Templates?

Different AI tools have different strengths. If you're not sure which tool to use:

- **Claude** excels at following complex, structured prompts like these
- **ChatGPT** is faster for simpler templates
- **Cursor** is best when the template involves editing code in your editor

We've written a [detailed comparison of Claude vs ChatGPT vs Cursor](/blog/claude-vs-chatgpt-vs-cursor) if you want to optimise your tool choice along with your prompts.

---

**Want to see how much quota these templates actually save you?** [Get QuotaMeter](https://quotameter.app) — track your usage across Cursor, Claude, ChatGPT, and APIs. See the difference better prompts make. £4.99 one-time.

Ready to track your AI usage?

Get QuotaMeter and never hit usage limits unexpectedly again.

Get QuotaMeter - £4.99