I've spent the last 3 months using AI coding assistants for everything — and I mean everything. Bug fixes, code reviews, test generation, documentation, even full feature implementation.
The problem? 80% of the prompts I see online are garbage. Vague instructions like "Write a Python script" that produce equally vague, broken code.
So I did something about it. I tested 500+ prompts across ChatGPT, Claude, Gemini, and Cursor. I kept the ones that consistently generated working, production-quality code. The rest got tossed.
Here are the 100 that survived — organized by use case.
1. Code Generation (20 prompts)
The Golden Rule: Always specify context
The single biggest mistake developers make with AI code generation is being vague. Here's the template that works every time:
Template: "Write a [language] [component] that [does X]. It receives [input] and returns [output]. Use [library/framework]. Include error handling for [edge cases]."
Production-ready examples:
Python — FastAPI endpoint with validation:
"Write a FastAPI endpoint that accepts a list of product IDs via POST, validates each ID against a SQLite DB, returns only the valid ones with their current prices. Use Pydantic models for validation. Include proper HTTP status codes."
JavaScript — Debounce function with TypeScript types:
"Write a strongly-typed debounce function in TypeScript. It should accept a generic callback function and a delay in ms. Return a debounced version that also preserves the 'this' context. Include proper cleanup for React hooks usage."
Python — Async web scraper with rate limiting:
"Create an async web scraper using aiohttp and BeautifulSoup. It should accept a list of URLs, respect robots.txt, limit to 5 requests/second, and save results to a JSON file. Include retry logic for failed requests."
Why these work:
- ✅ They specify the exact library (no guessing)
- ✅ They define clear inputs and outputs
- ✅ They request error handling upfront
- ✅ They mention edge cases
2. Debugging (15 prompts)
Debugging is where AI truly shines — if you give it the right context.
Template: "I'm getting [error] in my [language] code. Here's the relevant code: [code block]. The function should [expected behavior], but instead [actual behavior]. The input was [sample input], and I expected [expected output]. What's wrong?"
Pro tip: Include actual error traces
The difference between a useful debugging session and a time-waster is the error message. Always paste the full traceback. AI models are surprisingly good at parsing stack traces.
Template for stack trace debugging: "I'm getting this error in production: [paste full traceback]. Here's the code around line 42: [code]. The service was processing [context] when it failed. This happens intermittently about 1 in 100 requests."
3. Code Review (10 prompts)
AI code reviewers are surprisingly effective — they catch things human reviewers miss because they literally read every line.
Template: "Review this [language] code for production readiness. Look for: (1) security vulnerabilities, (2) performance bottlenecks, (3) race conditions, (4) memory leaks, (5) code smells. Here's the code: [code block]. The system handles [traffic volume] requests/day."
4. Test Generation (15 prompts)
Writing tests is the most tedious part of development. AI does it well.
Template: "Write pytest tests for this Python function. Include: (1) happy path, (2) edge cases, (3) error scenarios, (4) mocked external dependencies. The function: [code]. It uses [external API] for [purpose]."
For JavaScript/TypeScript:
"Write Jest tests for this React component. Test: (1) renders with default props, (2) handles empty state, (3) calls onClick handler, (4) updates on prop change. Component: [code]. It's used for [feature]."
5. Refactoring (10 prompts)
Template: "Refactor this [language] function for performance and readability. It currently [current behavior]. It's called [frequency] times per request and processes [data volume]. Prioritize (1) reducing memory usage, (2) improving readability, (3) adding type hints. Current code: [code]."
6. Documentation Generation (10 prompts)
Template: "Write README documentation for this Python package. Include: (1) installation instructions, (2) quick start example, (3) API reference with parameters, (4) troubleshooting guide for common errors. Here's the package code: [code]."
7. Database Queries (10 prompts)
Template: "Write a PostgreSQL query that [accomplishes X]. The schema is: [table definitions with types and indexes]. The query needs to handle [data volume] rows and complete in under [time limit]. Optimize for [use case]."
Example that works:
"Write a PostgreSQL query that finds the top 10 products by revenue in the last 30 days. The schema: orders(id, product_id, amount, created_at), products(id, name, category). We have 2M orders. The query should use an index and return the result in under 100ms."
8. Architecture & Design (10 prompts)
AI is surprisingly good at high-level design when given constraints.
Template: "Design a [system type] architecture for [use case]. Constraints: (1) must handle [scale], (2) budget of [$amount/month], (3) team of [size] developers, (4) must use [tech stack]. Consider: data flow, error handling, monitoring, and deployment strategy."
The Prompts That Failed (Don't Use These)
After 500 tests, here are the patterns that consistently produced bad code:
❌ "Write code for me" — Too vague. The AI guesses and usually guesses wrong.
❌ "Make it better" — No definition of "better." Faster? More readable? More secure?
❌ "Fix this" without context — The AI doesn't know what "working" looks like.
❌ "Like [famous project] but simpler" — The AI tries to replicate a complex system poorly.
How I Use These Prompts Daily
I've packaged these 100 prompts into a reusable collection called 100+ AI Coding Prompts for ChatGPT, Claude & Cursor — it includes all the templates above plus 80 more organized by language (Python, JS/TS, Go, Rust, SQL) and use case (build, debug, review, test, deploy).
Each prompt includes:
- The exact wording that works
- Expected output example
- Language-specific variations
- Common gotchas to avoid
Quick Reference: The 4 Templates That Cover 90% of Cases
1. GENERATE: "Write [language] [component] that [does X].
Input: [input]. Output: [output]. Use [library]."
2. DEBUG: "Error: [traceback]. Code: [relevant block].
Expected: [behavior]. Actual: [behavior]. What's wrong?"
3. REVIEW: "Review this for [security/perf/bugs].
Context: [traffic/data volume]. Code: [block]."
4. REFACTOR: "Refactor this for [speed/memory/readability].
Called [frequency]. Handles [volume]. Code: [block]."
What's your most-used AI coding prompt? Share it in the comments — I'm always testing new ones.
P.S. If you found this useful, I also maintain a collection of 50+ Python automation scripts that use these exact prompting patterns — copy-paste ready.
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