How to Write Better AI Prompts for Coding

Every developer using AI coding tools has experienced the frustration: you give the AI a prompt, it generates code, and the output is… not what you wanted. You try again with a slightly different phrasing, and it is still wrong. After the third attempt, you wonder if the tool is broken. It is not. The problem is almost always the prompt.

The difference between a developer who gets mediocre results from AI and one who gets exceptional results usually comes down to prompt quality. This is not about memorizing magic formulas — it is about understanding what information AI models need to produce useful code. In this guide, we will break down the anatomy of great coding prompts, share five proven patterns, walk through before-and-after examples, and cover tool-specific techniques for Claude Code, Cursor, and GitHub Copilot.

Why Prompt Quality Determines Output Quality

AI coding models are remarkably capable, but they are not mind readers. When you write “fix this bug,” the model has to guess what “this” refers to, what the expected behavior should be, and what constraints apply. It might guess correctly — or it might fix something that was not broken while ignoring the actual issue.

Research from both OpenAI and Anthropic consistently shows that prompt specificity correlates directly with output quality. A well-structured prompt gives the model the same information a senior developer would need to do the task: context about the system, a clear description of what needs to happen, constraints to work within, and the expected output format.

The good news: writing better prompts is a learnable skill, and once you internalize the patterns, it becomes second nature.

The Anatomy of a Great Coding Prompt

Every effective coding prompt contains four elements, whether explicitly stated or clearly implied:

You do not need to label these sections explicitly. The AI can parse natural language. But including all four elements dramatically improves results. Let us look at the difference:

Weak prompt: “Write a function to save user data”

Strong prompt: “Write a TypeScript server action for Next.js 14 that validates user profile updates using Zod (name: string 2-50 chars, email: valid email, bio: optional string max 500 chars). Return a discriminated union type for success/validation-error/server-error. Use our existing prisma client from @/lib/db.”

The weak prompt will produce a generic function that probably does not match your stack. The strong prompt will produce something you can use immediately.

5 Prompt Patterns That Work

1. The Bug Report Pattern

Use this when you need to diagnose and fix a bug. Structure your prompt like a bug report:

# The Bug Report Pattern

**What should happen:**
When a user submits the checkout form, their order should be saved
to the database and they should be redirected to /order/confirmation.

**What actually happens:**
The form submits successfully (200 response), but the redirect
never fires. The user stays on the checkout page. No errors in
the console.

**Steps to reproduce:**
1. Add items to cart
2. Go to /checkout
3. Fill in payment details
4. Click "Place Order"

**Relevant code:**
[paste the checkout handler, the redirect logic, and the form component]

**What I have already tried:**
- Checked that the redirect URL is correct
- Verified the order is actually saved to DB
- Added console.logs in the redirect handler (they fire)

This pattern works because it gives the AI the same information a human debugger would need. The “what I have already tried” section is especially valuable — it prevents the AI from suggesting things you have already ruled out.

2. The Specification Pattern

Use this when you need the AI to build something from scratch. Define inputs, outputs, and edge cases:

# The Specification Pattern

Create a `parseCSV` function in TypeScript:

**Input:** A string containing CSV data with headers in the first row.
**Output:** An array of objects where keys are header names and values
are the corresponding cell values.

**Requirements:**
- Handle quoted fields that contain commas: "Smith, John"
- Handle escaped quotes within quoted fields: "She said ""hello"""
- Trim whitespace from headers and values
- Return empty string for missing values (not undefined)
- Throw a descriptive error if the CSV has inconsistent column counts

**Examples:**
Input: "name,age\nAlice,30\nBob,25"
Output: [{name: "Alice", age: "30"}, {name: "Bob", age: "25"}]

Input: "name,city\n\"Smith, John\",\"New York\""
Output: [{name: "Smith, John", city: "New York"}]

**Edge cases to handle:**
- Empty input string (return empty array)
- Headers only, no data rows (return empty array)
- Trailing newline (ignore it)

The specification pattern produces significantly better code than “write a CSV parser” because the AI knows exactly what edge cases to handle and can verify its implementation against your examples.

3. The Refactoring Pattern

Use this when you have working code that needs improvement. Provide the current code, explain what is wrong with it, and specify your quality targets:

# The Refactoring Pattern

Refactor this Express route handler. Current problems:
1. Business logic is mixed with HTTP handling
2. Error handling is inconsistent (some errors return 500, some crash)
3. No input validation
4. Database queries are not transactional

Target state:
- Separate the handler into: validation layer, service layer, data layer
- All errors should return appropriate HTTP status codes with consistent
  JSON error format: { error: string, code: string, details?: object }
- Use Zod for input validation
- Wrap related DB operations in a transaction
- Keep the same external API contract (request/response format)

Current code:
[paste the code here]

For a comprehensive guide to AI-powered refactoring workflows, see our practical guide to AI code refactoring.

4. The Architecture Pattern

Use this when you need the AI to help design a system or feature. Provide requirements and constraints:

# The Architecture Pattern

Design a rate limiting system for our API with these requirements:

**Context:**
- Node.js API running on 3 instances behind a load balancer
- Redis available for shared state
- Current traffic: ~10K requests/minute
- Need to limit by: API key, IP address, and endpoint

**Requirements:**
- Sliding window rate limiting (not fixed window)
- Different limits per tier: free (100/hr), pro (1000/hr), enterprise (10000/hr)
- Return standard rate limit headers (X-RateLimit-Limit, Remaining, Reset)
- Graceful degradation if Redis is unavailable (allow traffic, log warning)

**Constraints:**
- Must work with our existing Express middleware chain
- Redis operations should be atomic (use Lua scripts if needed)
- Keep latency under 5ms per rate limit check

**Deliverables:**
1. Architecture overview (which components, how they interact)
2. Redis key schema
3. Implementation of the middleware
4. Tests for edge cases (window boundaries, Redis failures, concurrent requests)

5. The Review Pattern

Use this when you want the AI to review existing code. Specify what to look for and how to prioritize findings:

# The Review Pattern

Review this authentication module for security issues.

**Focus areas (in priority order):**
1. Authentication bypass vulnerabilities
2. Token handling (generation, storage, validation, expiry)
3. Input sanitization and injection prevention
4. Timing attack resistance
5. Error messages that leak information

**Severity levels:**
- CRITICAL: Exploitable vulnerability that must be fixed before deploy
- HIGH: Security weakness that should be addressed soon
- MEDIUM: Best practice violation that reduces security posture
- LOW: Suggestion for defense-in-depth improvement

**For each finding, provide:**
- Severity level
- Location (function name and line)
- Description of the issue
- Example exploit scenario (for CRITICAL/HIGH)
- Recommended fix with code

[paste the authentication module code]

Our AI code review tools comparison covers how different tools handle review workflows.

Before and After: Real Prompt Improvements

Example 1: Bug Fixing

Bad prompt:

Fix this bug in my login function

What the AI does: Guesses at what might be wrong. Might rewrite the entire function, changing things that worked. Often “fixes” something that was not the actual bug.

Good prompt:

The login function below returns "invalid credentials" even when
the password is correct. I verified the password hash matches in
the database. The issue started after I upgraded bcrypt from v5.0
to v5.1. The function works correctly if I downgrade bcrypt.

[paste the function]

Diagnose why the bcrypt upgrade broke password comparison and fix
it without downgrading. Keep backward compatibility with existing
password hashes in the database.

What the AI does: Focuses on the bcrypt version change, identifies the specific API difference between versions, and provides a targeted fix that maintains compatibility.

Example 2: Writing a Function

Bad prompt:

Write a function to validate emails

What the AI does: Produces a regex-based validator that either rejects valid emails (like [email protected]) or accepts invalid ones. You have no idea what standard it is following.

Good prompt:

Write a TypeScript email validation function that:
- Accepts: standard emails, plus-tagged emails ([email protected]),
  subdomains ([email protected])
- Rejects: missing @, missing domain, spaces, multiple @ signs
- Does NOT use regex (use a parser approach for maintainability)
- Returns { valid: boolean, reason?: string } so the caller can
  show specific error messages
- Include unit tests covering all the accept/reject cases above

What the AI does: Produces a parser-based validator with clear logic, typed return values, and comprehensive tests. You get something production-ready.

Example 3: Code Review

Bad prompt:

Review this code

What the AI does: Gives superficial feedback about variable naming and code style. Misses the actual issues because it does not know what matters to you.

Good prompt:

Review this payment processing function for:
1. Race conditions (this runs on multiple server instances)
2. Error handling (partial failures in the payment flow)
3. Data consistency (what happens if the charge succeeds but
   the order record fails to save?)

For each issue, explain the failure scenario and suggest a fix.
Ignore code style and naming - I only care about correctness
and reliability right now.

[paste the function]

What the AI does: Focuses exclusively on the areas that matter, provides specific failure scenarios, and suggests targeted fixes. The review is actionable instead of superficial.

Tool-Specific Prompt Tips

Claude Code

Claude Code uses a CLAUDE.md file for persistent project instructions. This is your biggest leverage point — put your coding standards, architecture decisions, and common patterns in CLAUDE.md so every interaction starts with the right context. For multi-turn workflows, Claude Code excels when you break complex tasks into sequential steps and let it execute autonomously. See our complete Claude Code setup guide for details.

Key prompting tips for Claude Code:

• Be explicit about which files to modify and which to leave alone
• Specify test requirements upfront (“run tests after each change”)
• Use the imperative mood: “Add error handling to the login function” not “Can you add error handling?”

Cursor

Cursor’s @ mention system is the key to effective prompting. Always provide context via @file, @folder, or @codebase references rather than pasting code manually. The .cursorrules file serves a similar purpose to CLAUDE.md — put your project-specific instructions there. For detailed Cursor techniques, see our Claude Code vs Cursor comparison.

Key prompting tips for Cursor:

• Use @codebase for questions that require understanding the full project
• Use Cmd+K for small, targeted edits (faster than chat for simple changes)
• In Composer mode, describe the end state, not the individual steps

GitHub Copilot

Copilot is unique because its primary prompting mechanism is your code itself. Comments, function signatures, and variable names all serve as prompts. For Copilot Chat, the prompting principles are the same as other tools, but inline Copilot works best when you write descriptive comments immediately before the code you want generated. See our GitHub Copilot review for current capabilities.

Key prompting tips for Copilot:

• Write the function signature and JSDoc comment first, then let Copilot complete the body
• Use descriptive variable names — they are part of the prompt
• If the first suggestion is wrong, add more specific comments and try again

Advanced Prompting Techniques

Chain-of-Thought Prompting

For complex problems, ask the AI to think through the problem step by step before writing code. This dramatically improves accuracy on tasks that require reasoning:

Before writing any code, analyze this problem step by step:
1. What are the possible states the system can be in?
2. What transitions between states are valid?
3. What error conditions need handling?
4. What are the performance implications?

Then implement the solution based on your analysis.

Few-Shot Examples

Show the AI examples of what you want before asking it to produce new output. This is especially useful for maintaining consistency:

Here are two existing API route handlers in our codebase:

[paste handler 1]
[paste handler 2]

Following the exact same pattern (validation, service call, error
handling, response format), create a new handler for the
DELETE /api/users/:id endpoint.

Role Prompting

Set the AI’s perspective for specialized tasks:

Act as a senior security engineer reviewing this code for
a SOC 2 compliance audit. Focus on data handling, access
controls, and audit logging. Flag anything that would fail
a compliance review.

Common Prompt Anti-Patterns

These are the five most damaging prompt habits we see developers repeat:

1. “Fix this” without context: The AI does not know what is broken. Always describe expected vs. actual behavior.
2. Pasting an entire file and saying “improve this”: The AI will make sweeping changes to things that were fine. Be specific about what needs improvement.
3. Asking for too many things at once: “Write a login system with OAuth, 2FA, rate limiting, and password recovery” will produce shallow implementations of everything. Break it into focused tasks.
4. Not specifying the tech stack: “Write a web server” could produce Python, Node.js, Go, or Rust code. Always state your language, framework, and version.
5. Ignoring constraints: If your function needs to run in under 100ms, or handle 10,000 concurrent users, or work without an internet connection — say so. The AI will optimize for different things depending on your constraints.

Frequently Asked Questions

How long should my prompts be?

There is no fixed rule, but effective prompts typically range from 3-10 sentences for simple tasks and 10-30 sentences for complex tasks. The goal is not brevity or length — it is providing the information the AI needs. A 5-sentence prompt with the right context beats a 50-sentence prompt full of irrelevant details.

Should I include my entire codebase as context?

No. Include the specific files and code relevant to the task. Too much context can actually reduce output quality because the model has to filter through irrelevant information. Tools like Cursor and Claude Code handle context selection automatically, but when manually providing context, be selective.

Do prompt techniques work the same across different AI models?

The core principles (specificity, context, constraints) work across all models. However, each model has strengths: Claude tends to follow instructions more precisely, GPT-4 is strong at creative solutions, and specialized coding models like Codestral are faster for simple completions. Adjust your expectations but not your fundamental technique.

How do I handle confidential code in prompts?

Never include secrets, API keys, or credentials in prompts. For proprietary code, use tools that offer privacy modes (Cursor Privacy Mode, self-hosted models). You can also abstract the problem: instead of pasting your actual payment processing code, describe the pattern and ask for a solution you can adapt.

What if the AI keeps producing wrong output despite good prompts?

Three strategies: (1) Break the task into smaller pieces and verify each step. (2) Switch models — different models handle different tasks better. (3) Provide a concrete example of the desired output format. If the AI consistently fails on a specific task, the task might require domain knowledge the model lacks, and a manual approach may be more efficient.