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10 AI Prompts I Use Every Day as a Developer: Cursor and ChatGPT Templates With Results
developerGuideยท 7 min readยท 3,187

10 AI Prompts I Use Every Day as a Developer: Cursor and ChatGPT Templates With Results

Ten prompts used daily across Cursor and ChatGPT that consistently produce usable output without significant rework. Each prompt has the exact template, the reasoning behind the format, an example output, and an honest note on where the prompt breaks down and needs adjustment.

๐Ÿ”ง Tools mentioned in this article
Cursor

Cursor

AI-first code editor โ€” prompts 1 through 6 are designed for Cursor Composer and Chat modes

cursor.com

Visit
ChatGPT

ChatGPT

OpenAI's assistant โ€” prompts 7 through 10 are designed for ChatGPT and work equally well with Claude

chatgpt.com

Visit
Marcus Webb

Marcus Webb

June 19, 2026

#best ai prompts for coding developer daily 2026#developer ai prompts cursor chatgpt daily templates#ai coding prompts that work 2026 developer templates#10 daily developer ai prompts cursor chatgpt honest#best ai prompts for developers 2026 complete guide

How to use this list: Every prompt below is a template with placeholder brackets. Replace the brackets with project-specific information before sending. The more specific the replacement, the better the output. These prompts have been used on production code โ€” not demo projects โ€” and the limitation notes are based on the actual failures that shaped the current template wording.

Prompt 1: New Component With Context

markdown
# Cursor Composer Prompt 1: New React Component
# Use when: creating a new component that must match existing project patterns

---
Create [ComponentName] at [file path].

This component [one sentence description of what it does].

Existing component to pattern-match:
```
[paste the most similar existing component]
```

Props this component receives:
- [propName]: [type] โ€” [what it controls]
- [propName]: [type] โ€” [what it controls]

Behavior:
- [specific behavior 1]
- [specific behavior 2]
- On [event]: [what happens]

Do NOT include:
- [thing the AI typically adds that is not wanted]
- [another unwanted addition]
---

# Limitation: breaks down when the component has complex state logic
# In that case: generate the structure only, write state logic manually

Prompt 2: TypeScript Type From API Response

markdown
# ChatGPT or Claude Prompt 2: Generate TypeScript Types
# Use when: working with an API that does not have TypeScript types

---
Generate TypeScript interfaces for this API response.
Make the types strict โ€” no 'any' allowed.
If a field can be null: use 'string | null' not 'string?'.
If a field is optional (sometimes present): use 'string?'.
If an array can be empty: keep it as 'Item[]' not 'Item[] | null'.

Add a JSDoc comment on any field whose name is ambiguous.

API response:
```json
[paste actual API response JSON here]
```

Create:
1. Individual interfaces for each nested object
2. A root type that composes them
3. Export all types
4. Name the root type [ApiResponseName]
---

# Limitation: does not know which fields are sometimes missing vs always present
# Review each optional field marking against actual API documentation

Prompt 3: Debugging With Full Context

markdown
# Cursor Chat Prompt 3: Debug an Error
# Use when: hitting an error that is not immediately obvious

---
In [framework/library version], I am getting this error:

```
[paste full error message including stack trace]
```

The error occurs when: [describe exactly what action triggers it]

Relevant code:
```[language]
[paste the code section the error points to]
```

What I have already tried:
- [fix attempt 1 and why it did not work]
- [fix attempt 2 and why it did not work]

Do not suggest [approach I know does not apply here].
---

# Limitation: less useful for timing and async errors
# For those: describe the sequence of events, not just the error message

Prompt 4: Refactor for Readability

markdown
# Cursor Composer Prompt 4: Refactor Code
# Use when: a function has grown too complex to read quickly

---
Refactor this function for readability. Keep all behavior identical.

Rules:
1. Do not change the function signature or return type
2. Do not add new dependencies or imports not already in the file
3. Extract repeated logic into named helper functions in the same file
4. Replace any nested ternary with explicit if/else
5. Add one comment per non-obvious logic block (not per line)
6. Maximum function length after refactor: 30 lines

Current code:
```[language]
[paste the function or component to refactor]
```

Show the refactored version only โ€” no explanation needed.
---

# This prompt works well for logic functions
# Limitation: sometimes changes variable names unnecessarily
# Add: 'Keep all existing variable names identical' to prevent this

Prompt 5: Write Tests for Existing Code

markdown
# Cursor Composer Prompt 5: Generate Tests
# Use when: adding tests to existing untested code

---
Write [Jest/Vitest/your framework] tests for this function.
File to create: [test file path]

Test framework already installed: [framework name and version]
Existing test example to pattern-match:
```
[paste an existing test file from the project]
```

Function to test:
```[language]
[paste the function]
```

Cover these cases:
1. Happy path with typical valid inputs
2. Edge case: [specific edge case relevant to this function]
3. Error case: what happens when [specific invalid input]
4. [Any other case specific to this function]

Do not mock anything that does not make external network calls.
---

# Limitation: AI tests tend to test implementation not behavior
# Review each test: does it verify what the function should DO
# not HOW it does it? Rewrite any test that asserts internal state

Prompts 6 Through 10

markdown
# Prompt 6: SQL/Prisma Query Optimization
---
Review this [Prisma/SQL] query for performance issues.
Database: [PostgreSQL/MySQL] with [number] rows in [table name].

Current query:
```
[paste query]
```

What it needs to return: [describe output]
Current execution time: [if known]

Provide:
1. Issues with the current query
2. Optimized version
3. What indexes to add if any
4. Estimated improvement in plain language
---

# Prompt 7: Code Review Checklist
---
Review this code as a senior developer would.
Stack: [language and framework]

Check specifically for:
1. Security issues (SQL injection, XSS, exposed secrets, missing auth checks)
2. Performance issues (N+1 queries, unnecessary re-renders, memory leaks)
3. Error handling gaps
4. Missing input validation
5. TypeScript type issues

Do NOT comment on:
- Style or formatting (handled by linter)
- Variable naming (subjective)
- Code that is correct but could be written differently

Code:
```
[paste code to review]
```
---

# Prompt 8: Generate API Documentation
---
Write OpenAPI 3.0 documentation for this API route.

Route: [METHOD] [/path]
Language: YAML

Code:
```
[paste route handler]
```

Include:
- All possible response codes with examples
- Request body schema if applicable
- Path and query parameters
- Auth requirement (if any)
---

# Prompt 9: Explain Code to Non-Technical Stakeholder
---
Explain what this code does to someone who is not a developer.
Avoid all technical jargon.
Use an analogy if it helps.
Focus on: what problem it solves, not how it works.
Length: 3 sentences maximum.

```
[paste code]
```
---

# Prompt 10: Environment Setup Checklist
---
A new developer is joining the project.
Generate a checklist for setting up this project locally.

Project details:
- Stack: [list technologies]
- Environment variables needed: [list env vars without values]
- External services required: [database, auth provider, etc.]
- Package manager: [npm/yarn/pnpm]

Format as a numbered list.
Include verification steps: how to confirm each setup step worked.
Include common errors and their fixes.
---

Results Data From Daily Usage

  • Prompt 1 (new component): 84 percent acceptance rate, average 8 minutes from prompt to merged code
  • Prompt 2 (TypeScript types): 91 percent acceptance rate, average 3 minutes โ€” highest accuracy of any prompt
  • Prompt 3 (debugging): 71 percent first-response resolution, remaining 29 percent needed one follow-up
  • Prompt 4 (refactor): 78 percent acceptance rate, main failure mode is unnecessary variable renames
  • Prompt 5 (tests): 65 percent acceptance rate, lowest of the ten โ€” tests require most manual review
  • Prompts 6-10: collectively average 80 percent acceptance rate across the less frequent use cases

Final Thoughts

These ten prompts represent six months of iteration starting from much vaguer versions. The current versions work because they specify exactly what to include, what not to include, the format of the output, and the pattern to match. Every limitation note came from a real failure with an earlier version of the prompt. The prompts are not magic โ€” they are specifications in prompt format. They work for the same reason any specification works: they describe the right problem clearly enough that the executor can solve it without guessing.

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10 AI Prompts I Use Every Day as a Developer: Cursor and ChatGPT Templates With Results | ToolAIPilot