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# 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. Nevertheless, the code AI generates is usually a ache to keep up.
In case you are utilizing instruments like Claude Code, GitHub Copilot, or Cursor’s agentic mode, you might have in all probability skilled this. The AI helps you ship working code quick, however the fee exhibits up later. You will have probably refactored a bloated operate simply to grasp 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 easy methods to bridge this hole with a deal with Python-specific methods.
# Avoiding the Clean Canvas Entice
The largest mistake builders make is asking AI to start out from scratch. AI brokers work greatest with constraints and pointers.
Earlier than you write your first immediate, arrange the fundamentals of the mission your self. This implies selecting your mission construction — putting in your core libraries and implementing a couple of working examples — to set the tone. This might sound counterproductive, but it surely helps with getting AI to write down code that aligns higher with what you want in your software.
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 Person mannequin are outlined elsewhere
async def get_user(user_id: int, db: Session = Relies upon(get_db)):
consumer = db.question(Person).filter(Person.id == user_id).first()
if not consumer:
elevate HTTPException(status_code=404, element="Person not discovered")
return consumer
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 mission construction. Create your directories, arrange your imports, and configure your testing framework. AI shouldn’t be making these architectural choices.
# Making Python’s Sort 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 kind hints important guardrails as an alternative of a nice-to-have in your software code.
Strict typing catches AI errors earlier than they attain manufacturing. Whenever you require kind 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 may be strings or may be lists.
Extra importantly, strict sorts pressure higher design. For instance, an AI agent making an attempt to write down a operate that accepts information: dict could make many assumptions about what’s in that dictionary. Nevertheless, 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 write down 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:
cross
# Slightly than this
def process_user(information: dict) -> dict:
cross
Use libraries that implement contracts: SQLAlchemy 2.0 with type-checked fashions and FastAPI with response fashions are wonderful selections. These aren’t simply good practices; they’re constraints that hold AI on monitor.
Set mypy to strict mode and make passing kind checks non-negotiable. When AI generates code that fails kind checking, it is going to iterate till it passes. This automated suggestions loop produces higher code than any quantity of immediate engineering.
# Creating Documentation to Information AI
Most initiatives 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 mission root. Don’t make it too lengthy. Deal with what is exclusive about your mission moderately than basic Python greatest practices.
Your AI pointers ought to specify:
- Undertaking 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)
- Specific forbidden patterns
- Testing necessities
Right here is an instance AGENTS.md file:
# Undertaking Tips
## 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 means of repository sample (see src/repositories/)
- Use dependency injection for all exterior dependencies
## Requirements
- Sort hints on all capabilities
- Docstrings utilizing Google model
- Features underneath 50 traces
- Run `mypy --strict` and `ruff examine` earlier than committing
## By no means
- No naked besides clauses
- No kind: ignore feedback
- No mutable default arguments
- No world state
The hot button is being particular. Don’t merely say “observe greatest 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 a substitute of asking AI to “add authentication,” stroll it by means 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. Comply with 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 much like consumer.py.
Add pytest exams in exams/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’re asking it to use what it’s essential to a brand new function.
When the AI generates code, evaluation it in opposition to your patterns. Does it use the identical dependency injection method? Does it observe the identical error dealing with? Does it arrange imports the identical method? 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 might sometimes make them much less helpful if velocity 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 suppose by means of dependencies and construction. It additionally provides you an opportunity to catch architectural issues — resembling 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 exams are wanted
Evaluate this plan such as you would evaluation a design doc. Verify that the AI understands your mission construction. Confirm it’s utilizing the suitable libraries and ensure it’s not reinventing one thing that already exists.
If the plan seems good, let the AI execute it. If not, right the plan earlier than any code will get written. It’s simpler to repair a foul plan than to repair unhealthy code.
# Asking AI to Write Assessments That Really Check
AI is nice and tremendous quick at writing exams. Nevertheless, AI shouldn’t be environment friendly at writing helpful exams except you’re particular about what “helpful” means.
Default AI check conduct is to check the comfortable path and nothing else. You get exams that confirm the code works when every thing goes proper, which is precisely when you don’t want exams.
Specify your testing necessities explicitly. For each function, require:
- Glad path check
- Validation error exams to examine what occurs with invalid enter
- Edge case exams for empty values, None, boundary circumstances, and extra
- Error dealing with exams for database failures, exterior service failures, and the like
Level AI to your present check information as examples. When you’ve got good check patterns already, AI will write helpful exams, too. Should you shouldn’t have good exams but, write a couple of your self first.
# Validating Output Systematically
After AI generates code, don’t simply examine if it runs. Run it by means of a guidelines.
Your validation guidelines ought to embrace questions like the next:
- Does it cross mypy strict mode
- Does it observe patterns from present code
- Are all capabilities underneath 50 traces
- Do exams cowl edge instances and errors
- Are there kind 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 not automate, you’ll spot frequent anti-patterns after reviewing sufficient AI code — resembling capabilities that do an excessive amount of, error dealing with that swallows exceptions, or validation logic blended with enterprise logic.
# Implementing a Sensible Workflow
Allow us to now put collectively every thing we’ve got mentioned up to now.
You begin a brand new mission. 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 kind checking and exams. Every little thing passes. You evaluation the code in opposition to your patterns. It matches. You commit.
Complete time from immediate to commit could solely be round quarter-hour for a function that might have taken you an hour to write down manually. However extra importantly, the code you get is less complicated to keep up — it follows the patterns you established.
The subsequent function goes quicker 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 ability that issues most shouldn’t be writing code quicker. Slightly, it’s designing methods that constrain AI to write down maintainable code. It’s figuring out 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 selection. Tell us what else you suppose we will do to maintain AI-generated Python code maintainable. Hold 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 embrace DevOps, information science, and pure language processing. She enjoys studying, writing, coding, and occasional! At present, she’s engaged on studying and sharing her data with the developer neighborhood by authoring tutorials, how-to guides, opinion items, and extra. Bala additionally creates partaking useful resource overviews and coding tutorials.
