Picture by Creator
# Introduction
AI coding instruments are getting impressively good at writing Python code that works. They’ll construct complete purposes and implement advanced algorithms in minutes. Nonetheless, the code AI generates is commonly a ache to keep up.
In case you are utilizing instruments like Claude Code, GitHub Copilot, or Cursor’s agentic mode, you will have in all probability skilled this. The AI helps you ship working code quick, however the associated fee exhibits up later. You may have probably refactored a bloated operate simply to know the way it works weeks after it was generated.
The issue is not that AI writes unhealthy code — although it generally does — it’s that AI optimizes for “working now” and finishing the necessities in your immediate, when you want code that’s readable and maintainable in the long run. This text exhibits you the way to bridge this hole with a give attention to Python-specific methods.
# Avoiding the Clean Canvas Lure
The most important mistake builders make is asking AI to begin from scratch. AI brokers work finest with constraints and pointers.
Earlier than you write your first immediate, arrange the fundamentals of the challenge your self. This implies selecting your challenge construction — putting in your core libraries and implementing a number of working examples — to set the tone. This may appear counterproductive, but it surely helps with getting AI to put in writing code that aligns higher with what you want in your utility.
Begin by constructing a few options manually. In case you are constructing an API, implement one full endpoint your self with all of the patterns you need: dependency injection, correct error dealing with, database entry, and validation. This turns into the reference implementation.
Say you write this primary endpoint manually:
from fastapi import APIRouter, Relies upon, HTTPException
from sqlalchemy.orm import Session
router = APIRouter()
# Assume get_db and Consumer mannequin are outlined elsewhere
async def get_user(user_id: int, db: Session = Relies upon(get_db)):
person = db.question(Consumer).filter(Consumer.id == user_id).first()
if not person:
increase HTTPException(status_code=404, element="Consumer not discovered")
return person
When AI sees this sample, it understands how we deal with dependencies, how we question databases, and the way we deal with lacking data.
The identical applies to your challenge construction. Create your directories, arrange your imports, and configure your testing framework. AI shouldn’t be making these architectural choices.
# Making Python’s Kind System Do the Heavy Lifting
Python’s dynamic typing is versatile, however that flexibility turns into a legal responsibility when AI is writing your code. Make sort hints important guardrails as an alternative of a nice-to-have in your utility code.
Strict typing catches AI errors earlier than they attain manufacturing. If you require sort hints on each operate signature and run mypy in strict mode, the AI can not take shortcuts. It can not return ambiguous sorts or settle for parameters that could be strings or could be lists.
Extra importantly, strict sorts power higher design. For instance, an AI agent attempting to put in writing a operate that accepts information: dict could make many assumptions about what’s in that dictionary. Nonetheless, an AI agent writing a operate that accepts information: UserCreateRequest the place UserCreateRequest is a Pydantic mannequin has precisely one interpretation.
# This constrains AI to put in writing right code
from pydantic import BaseModel, EmailStr
class UserCreateRequest(BaseModel):
title: str
e mail: EmailStr
age: int
class UserResponse(BaseModel):
id: int
title: str
e mail: EmailStr
def process_user(information: UserCreateRequest) -> UserResponse:
move
# Slightly than this
def process_user(information: dict) -> dict:
move
Use libraries that implement contracts: SQLAlchemy 2.0 with type-checked fashions and FastAPI with response fashions are glorious decisions. These usually are not simply good practices; they’re constraints that maintain AI on monitor.
Set mypy to strict mode and make passing sort checks non-negotiable. When AI generates code that fails sort checking, it’s going to iterate till it passes. This computerized suggestions loop produces higher code than any quantity of immediate engineering.
# Creating Documentation to Information AI
Most tasks have documentation that builders ignore. For AI brokers, you want documentation they really use — like a README.md file with pointers. This implies a single file with clear, particular guidelines.
Create a CLAUDE.md or AGENTS.md file at your challenge root. Don’t make it too lengthy. Concentrate on what is exclusive about your challenge reasonably than common Python finest practices.
Your AI pointers ought to specify:
- Venture construction and the place various kinds of code belong
- Which libraries to make use of for frequent duties
- Particular patterns to observe (level to instance information)
- Express forbidden patterns
- Testing necessities
Right here is an instance AGENTS.md file:
# Venture Pointers
## Construction
/src/api - FastAPI routers
/src/providers - enterprise logic
/src/fashions - SQLAlchemy fashions
/src/schemas - Pydantic fashions
## Patterns
- All providers inherit from BaseService (see src/providers/base.py)
- All database entry goes by way of repository sample (see src/repositories/)
- Use dependency injection for all exterior dependencies
## Requirements
- Kind hints on all capabilities
- Docstrings utilizing Google type
- Features underneath 50 strains
- Run `mypy --strict` and `ruff verify` earlier than committing
## By no means
- No naked besides clauses
- No sort: ignore feedback
- No mutable default arguments
- No world state
The secret is being particular. Don’t merely say “observe finest practices.” Level to the precise file that demonstrates the sample. Don’t solely say “deal with errors correctly;” present the error dealing with sample you need.
# Writing Prompts That Level to Examples
Generic prompts produce generic code. Particular prompts that reference your present codebase produce extra maintainable code.
As an alternative of asking AI to “add authentication,” stroll it by way of the implementation with references to your patterns. Right here is an instance of such a immediate that factors to examples:
Implement JWT authentication in src/providers/auth_service.py. Observe the identical construction as UserService in src/providers/user_service.py. Use bcrypt for password hashing (already in necessities.txt).
Add authentication dependency in src/api/dependencies.py following the sample of get_db.
Create Pydantic schemas in src/schemas/auth.py just like person.py.
Add pytest assessments in assessments/test_auth_service.py utilizing fixtures from conftest.py.
Discover how each instruction factors to an present file or sample. You aren’t asking AI to construct out an structure; you might be asking it to use what you’ll want to a brand new function.
When the AI generates code, evaluation it in opposition to your patterns. Does it use the identical dependency injection strategy? Does it observe the identical error dealing with? Does it set up imports the identical means? If not, level out the discrepancy and ask it to align with the prevailing sample.
# Planning Earlier than Implementing
AI brokers can transfer quick, which may sometimes make them much less helpful if pace comes on the expense of construction. Use plan mode or ask for an implementation plan earlier than any code will get written.
A planning step forces the AI to assume by way of dependencies and construction. It additionally provides you an opportunity to catch architectural issues — equivalent to round dependencies or redundant providers — earlier than they’re carried out.
Ask for a plan that specifies:
- Which information shall be created or modified
- What dependencies exist between parts
- Which present patterns shall be adopted
- What assessments are wanted
Assessment this plan such as you would evaluation a design doc. Test that the AI understands your challenge construction. Confirm it’s utilizing the appropriate libraries and make sure it isn’t reinventing one thing that already exists.
If the plan seems to be good, let the AI execute it. If not, right the plan earlier than any code will get written. It’s simpler to repair a nasty plan than to repair unhealthy code.
# Asking AI to Write Checks That Really Take a look at
AI is nice and tremendous quick at writing assessments. Nonetheless, AI just isn’t environment friendly at writing helpful assessments until you might be particular about what “helpful” means.
Default AI check habits is to check the blissful path and nothing else. You get assessments that confirm the code works when the whole lot goes proper, which is precisely when you do not want assessments.
Specify your testing necessities explicitly. For each function, require:
- Glad path check
- Validation error assessments to verify what occurs with invalid enter
- Edge case assessments for empty values, None, boundary circumstances, and extra
- Error dealing with assessments for database failures, exterior service failures, and the like
Level AI to your present check information as examples. If in case you have good check patterns already, AI will write helpful assessments, too. For those who should not have good assessments but, write a number of your self first.
# Validating Output Systematically
After AI generates code, don’t simply verify if it runs. Run it by way of a guidelines.
Your validation guidelines ought to embody questions like the next:
- Does it move mypy strict mode
- Does it observe patterns from present code
- Are all capabilities underneath 50 strains
- Do assessments cowl edge circumstances and errors
- Are there sort hints on all capabilities
- Does it use the desired libraries accurately
Automate what you may. Arrange pre-commit hooks that run mypy, Ruff, and pytest. If AI-generated code fails these checks, it doesn’t get dedicated.
For what you can’t automate, you’ll spot frequent anti-patterns after reviewing sufficient AI code — equivalent to capabilities that do an excessive amount of, error dealing with that swallows exceptions, or validation logic combined with enterprise logic.
# Implementing a Sensible Workflow
Allow us to now put collectively the whole lot we’ve got mentioned so far.
You begin a brand new challenge. You spend time organising the construction, selecting and putting in libraries, and writing a few instance options. You create CLAUDE.md along with your pointers and write particular Pydantic fashions.
Now you ask AI to implement a brand new function. You write an in depth immediate pointing to your examples. AI generates a plan. You evaluation and approve it. AI writes the code. You run sort checking and assessments. The whole lot passes. You evaluation the code in opposition to your patterns. It matches. You commit.
Whole time from immediate to commit might solely be round quarter-hour for a function that may have taken you an hour to put in writing manually. However extra importantly, the code you get is simpler to keep up — it follows the patterns you established.
The subsequent function goes sooner as a result of AI has extra examples to be taught from. The code turns into extra constant over time as a result of each new function reinforces the prevailing patterns.
# Wrapping Up
With AI coding instruments proving tremendous helpful, your job as a developer or an information skilled is altering. You at the moment are spending much less time writing code and extra time on:
- Designing methods and selecting architectures
- Creating reference implementations of patterns
- Writing constraints and pointers
- Reviewing AI output and sustaining the standard bar
The talent that issues most just isn’t writing code sooner. Slightly, it’s designing methods that constrain AI to put in writing maintainable code. It’s realizing which practices scale and which create technical debt. I hope you discovered this text useful even when you don’t use Python as your programming language of alternative. Tell us what else you assume we are able to do to maintain AI-generated Python code maintainable. Preserve exploring!
Bala Priya C is a developer and technical author from India. She likes working on the intersection of math, programming, information science, and content material creation. Her areas of curiosity and experience embody DevOps, information science, and pure language processing. She enjoys studying, writing, coding, and low! Presently, she’s engaged on studying and sharing her information with the developer neighborhood by authoring tutorials, how-to guides, opinion items, and extra. Bala additionally creates partaking useful resource overviews and coding tutorials.
