If you’re using AI coding assistants without type hints, you’re leaving performance on the table.
When you ask an AI to complete this:
def process(data):
# TODO: split by comma and return uppercase words
The AI has to guess what data is. A string? A file? A list?
But with type hints:
def process(data: str) -> list[str]:
# TODO: split by comma and return uppercase words
Now the AI knows:
-
datais definitely a string - It should return a list of strings
- Methods like
.split()and.upper()are appropriate
The result: More accurate completions, fewer hallucinations, less back-and-forth.
This extends to entire codebases. When your functions have type hints, AI tools can:
- Generate code that matches your existing types
- Suggest appropriate methods for the given types
- Catch inconsistencies in their own output
- Understand relationships between modules
Where this matters most:
- API handlers (FastAPI uses type hints for automatic validation)
- Data processing pipelines
- Any code that interfaces with AI-generated components
Type hints are documentation for both humans and machines. In 2026, that dual purpose matters more than ever.
This is adapted from my upcoming book, Zero to AI Engineer: Python Foundations.
I share excerpts like this on Substack → https://substack.com/@samuelochaba