Asking AI to Generate Code

Code generation prompts that include specific requirements, language, and constraints produce more useful and accurate code than vague requests.

12 min•By Priygop Team•Updated 2026

Code Generation Prompt Structure

A good code generation prompt includes:

1Language: Python, JavaScript, SQL, etc.
2Task: exactly what the function or script should do
3Input: what data goes in
4Output: what should be returned
5Constraints: performance, style, libraries to use or avoid
6Error handling: how to handle edge cases
7Comments: whether to include explanatory comments

Diagram

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Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence

Code Generation Prompt Examples

# Code generation prompt examples

# Basic function
basic_function = """
Write a Python function called calculate_average.
Input: a list of numbers.
Output: the average as a float.
Handle the edge case where the list is empty by returning 0.
Include a docstring explaining what the function does.
"""

# More complex
complex_task = """
Write a JavaScript function called filterUniqueEmails.
Input: an array of email strings that may contain duplicates.
Output: a new array with only unique emails, lowercase.
Do not modify the original array.
Include a comment explaining the approach used.
"""

# With constraints
constrained_task = """
Write a Python function that reads a CSV file and returns
the data as a list of dictionaries.
Use only Python's built-in csv module (no pandas).
Each dictionary key should be the column name.
Include error handling for file not found.
"""

print("Specific code generation prompts produce better starting code.")
print("Always test the generated code with your own test cases.")

Key Takeaways

Always specify the programming language in code generation prompts
Define inputs, outputs, and edge cases for the best results
Specify which libraries to use or avoid
Request comments when you want the code to be educational
Always test AI-generated code; never use it in production without review