The Developer's Guide to Writing Better Prompts for Coding Assistants

The Reality: You ask your AI coding assistant to "create a function," and it gives you something generic that doesn't fit your needs. You spend more time fixing the output than you would have writing it yourself.

Sound familiar?

The problem isn't the AI—it's the prompt. AI coding assistants are incredibly powerful, but they need context, constraints, and clarity to deliver exactly what you need.

This guide will show you how to write prompts that get you production-ready code on the first try.

Why Most Prompts Fail

The Generic Prompt Problem

Bad Prompt:

Write a function to validate JSON.

What You Get: A basic function that checks if a string is valid JSON.

What You Actually Needed: A function that validates JSON against a specific schema, returns detailed error messages, and handles edge cases.

The Missing Context Problem

AI assistants don't know:

• Your tech stack
• Your coding style
• Your project constraints
• Your error handling preferences

Without this context, they make assumptions—and those assumptions are often wrong.

The 5 Elements of a Perfect Prompt

1. Context (What You're Building)

Bad:

Create a user authentication function.

Good:

I'm building a Next.js 14 app with TypeScript. Create a server-side 
authentication function that validates JWT tokens from cookies.

Why It Works: The AI now knows your framework, language, and where the function will run.

2. Constraints (Technical Requirements)

Bad:

Write a function to fetch data from an API.

Good:

Write a TypeScript function to fetch user data from a REST API. 
Requirements:
- Use native fetch (no axios)
- Handle 401 errors by redirecting to /login
- Return typed data using a User interface
- Include retry logic (max 3 attempts)

Why It Works: Clear constraints eliminate guesswork and prevent generic solutions.

3. Format (How You Want the Output)

Bad:

Show me how to parse JSON in Python.

Good:

Write a Python function called parse_json_safe() that:
- Takes a string parameter
- Returns a tuple: (success: bool, data: dict | None, error: str | None)
- Includes docstring with examples
- Uses type hints

Why It Works: You get exactly the structure you need, ready to paste into your codebase.

4. Examples (Show, Don't Just Tell)

Bad:

Create a function to transform user data.

Good:

Create a JavaScript function to transform user data.

Input example:
{
  "first_name": "John",
  "last_name": "Doe",
  "email_address": "john@example.com"
}

Expected output:
{
  "fullName": "John Doe",
  "email": "john@example.com"
}

Why It Works: Examples eliminate ambiguity and show the exact transformation logic.

5. Edge Cases (What Could Go Wrong)

Bad:

Write a function to divide two numbers.

Good:

Write a TypeScript function to divide two numbers.
Handle these edge cases:
- Division by zero (return null)
- Non-numeric inputs (throw TypeError)
- Infinity results (return null)
- Return type should be number | null

Why It Works: You get production-ready code that won't crash in unexpected scenarios.

Copy-Paste Prompt Templates

Template 1: API Integration Function

I'm building a [FRAMEWORK] app with [LANGUAGE]. Create a function to [ACTION].

Context:
- Framework: [Next.js/React/Express/etc.]
- Language: [TypeScript/JavaScript/Python]
- Environment: [Client-side/Server-side]

Requirements:
- Use [LIBRARY/APPROACH]
- Handle [ERROR_TYPES] errors
- Return [DATA_TYPE]
- Include [SPECIFIC_FEATURES]

Example input: [EXAMPLE]
Expected output: [EXAMPLE]

Template 2: Data Transformation

Create a [LANGUAGE] function to transform [DATA_TYPE] from [SOURCE_FORMAT] to [TARGET_FORMAT].

Input structure:
[EXAMPLE_INPUT]

Output structure:
[EXAMPLE_OUTPUT]

Rules:
- [TRANSFORMATION_RULE_1]
- [TRANSFORMATION_RULE_2]
- Handle missing fields by [STRATEGY]

Template 3: Validation Function

Write a [LANGUAGE] validation function for [DATA_TYPE].

Validation rules:
- [RULE_1]
- [RULE_2]
- [RULE_3]

Return format:
- On success: { valid: true, data: [TYPE] }
- On failure: { valid: false, errors: string[] }

Include unit tests for:
- Valid input
- [EDGE_CASE_1]
- [EDGE_CASE_2]

Real-World Example: Before & After

Before (Vague Prompt)

Create a function to validate email addresses.

Result: A basic regex check that doesn't handle edge cases.

After (Precise Prompt)

Create a TypeScript function called validateEmail() that:

Context:
- Used in a Next.js 14 registration form
- Runs on the client side

Requirements:
- Check format using RFC 5322 standard
- Reject disposable email domains (tempmail.com, etc.)
- Return { valid: boolean, error?: string }
- Include JSDoc comments

Edge cases:
- Emails with + symbols (valid)
- Emails with subdomains (valid)
- Emails without TLD (invalid)

Example:
validateEmail("user+tag@example.com") 
// => { valid: true }

Result: Production-ready code with proper validation, error messages, and documentation.

Advanced Techniques

Chain of Thought Prompting

Instead of asking for the final code, ask the AI to think through the problem first:

Before writing code, explain:
1. What data structure is best for this problem?
2. What edge cases should we handle?
3. What's the time complexity?

Then write the implementation.

Iterative Refinement

Start broad, then refine:

Step 1: "Create a basic user authentication flow"
Step 2: "Add JWT token validation"
Step 3: "Add refresh token logic"
Step 4: "Add rate limiting"

Role-Based Prompting

You are a senior TypeScript developer who prioritizes type safety and error handling.
Create a function to...

Common Mistakes to Avoid

1. Assuming the AI Knows Your Stack

❌ "Create a component"
✅ "Create a React functional component with TypeScript"

2. Not Specifying Error Handling

❌ "Fetch data from an API"
✅ "Fetch data from an API and handle network errors, 404s, and timeouts"

3. Forgetting Type Safety

❌ "Parse this JSON"
✅ "Parse this JSON and return a typed User object"

4. No Examples

❌ "Transform this data"
✅ "Transform this data: [input] → [output]"

Measuring Prompt Quality

A good prompt should:

• ✅ Generate code that works on the first try
• ✅ Require minimal modifications
• ✅ Include proper error handling
• ✅ Match your coding style
• ✅ Include documentation/comments

If you're constantly fixing AI-generated code, your prompts need work.

Conclusion

Writing better prompts is a skill that pays immediate dividends. By providing context, constraints, format requirements, examples, and edge cases, you transform AI coding assistants from "sometimes helpful" to "indispensable."

Key Takeaways:

• Always provide context (framework, language, environment)
• Specify constraints and requirements explicitly
• Show examples of input and output
• Define error handling and edge cases
• Use templates for consistency

Bonus: Prompt Template Library

Save these templates for your next coding session:

API Client:

Create a [LANGUAGE] API client for [SERVICE].
Include: authentication, error handling, retry logic, and TypeScript types.

Database Query:

Write a [ORM] query to [ACTION].
Return: [TYPE]
Handle: [EDGE_CASES]

Unit Test:

Write unit tests for [FUNCTION] using [TESTING_FRAMEWORK].
Test cases: happy path, [EDGE_CASE_1], [EDGE_CASE_2]

Start using these techniques today, and watch your AI-assisted coding productivity skyrocket.