AI Doesn’t Write Bad Code. It Just Write Too Much of It.
The Wise-Owl Guide to Human-AI Coding Partnerships That Actually Ship
After six months of daily AI-assisted coding, I've debugged everything from mysteriously failing data pipelines to authentication systems that nobody asked for. The lesson? Working with AI is like managing a brilliant intern: full of book knowledge, zero real-world scars, and absolutely dangerous if left unsupervised.
The 8 Rules of AI Coding That Actually Ship
Stay the Boss: never let AI design your system
Question Everything: stop improvisation before it hits code
One Bite at a Time: prevent monster functions
Enforce Safety: never trust, always verify
Set Context Clearly: talk to AI like a contractor
Cross-Validate Across AIs: get second opinions for critical logic
Clean Up the Chatter: strip comment bloat
Enforce Quality Gates: clear contracts for every function
Through countless hours of trial, error, and the occasional production incident that shall remain nameless, I've developed this practical playbook for human-AI coding partnerships that actually work. Not the theoretical "trust but verify" advice you see everywhere, but the tactical, battle-tested rules that help you ship code instead of debugging AI's creative interpretations of your requirements.
The Fundamental Problem: AI Optimizes for Completeness, Not Context
Before diving into tactics, let's understand why AI consistently over-engineers solutions. These models are trained on examples that prioritize completeness and best practices: the kind of code that gets upvoted on Stack Overflow or starred on GitHub. They're not trained on the messy reality of "I need this working by 2 PM" or "this is a one-off analysis script, not the next microservice architecture."
AI doesn't understand that sometimes good enough is actually good. It doesn't know the difference between a quick data exploration and a mission-critical system. Every problem gets the enterprise treatment because that's what looks "correct" in training data.
This creates the core tension: AI wants to build you a cathedral when you asked for a shed.
Rule 1: Stay the Boss — Never Let AI Design Your System
Never let AI drive architectural decisions. Use it for brainstorming and code generation, but maintain creative control over system design.
The most expensive mistake I see developers make is handing over the architectural reins. Last month, I asked for a simple data validator: something to check if a CSV had the required columns before processing. What I got back was a full enterprise platform complete with:
A REST API layer with authentication
A Docker containerization strategy
A web dashboard for monitoring validation status
All I wanted was a function that returned True or False.
Now I lead every interaction with explicit constraints: "I need a function, not an enterprise solution." This single phrase has saved me hours of refactoring AI's ambitious solutions back to reality.
The Pattern:
# What I ask for:
"Write a function to validate CSV columns"
# What AI wants to build:
class ValidationFramework:
def __init__(self, config_path, logger, db_connection, cache_backend):
# 200 lines later...
# What I actually need:
def validate_columns(df, required_cols):
return all(col in df.columns for col in required_cols)
AI is phenomenal for brainstorming solutions and generating boilerplate code. It can suggest approaches you might not have considered and catch edge cases you missed. But the moment you let it design your system architecture or choose your tech stack, you're signing up for weeks of complexity debt.
Watch for dependency creep. AI will happily suggest new libraries for trivial jobs. If you wouldn't introduce a new dependency without team discussion in real development, don't let AI sneak one in. I've seen AI recommend entire frameworks to solve problems that needed three lines of vanilla code.
Rule 2: Question Everything — Stop AI Improvisation Before It Hits Code
Force AI to explain reasoning before changing code. AI improvises more than you think.
I was working on a data transformation script and asked AI to help optimize a slow function. Instead of just improving the performance, it decided to:
Change the output format "for better downstream compatibility"
Add error handling that silently skipped bad rows
Implement caching that I never requested
Modify the function signature to accept additional parameters
None of this was in my prompt. AI saw an optimization request and took creative liberties with the entire function design.
Now I'm explicit about boundaries: "Answer the question, but do not change code until I say so." This forces AI to explain its reasoning before implementing changes. You can catch scope creep before it becomes code bloat.
Practical Implementation:
Instead of: "How can I make this function faster?" Try: "Analyze this function's performance bottlenecks. Explain what you'd change and why, but don't modify the code yet."
This approach transforms AI from an autonomous code modifier into a collaborative thinking partner.
Rule 3: One Bite at a Time — Prevent Monster Functions
Feed AI small, single-responsibility problems. Define interfaces first, then let it implement. This prevents functions that try to solve world hunger.
AI has an irresistible urge to solve every problem in a single, comprehensive function. Ask for a data processing script, and you'll get something that loads data, validates it, transforms it, trains a model, generates reports, sends notifications, and probably orders lunch.
The antidote is aggressive problem decomposition. Feed AI bite-sized, single-responsibility problems:
Instead of: "Write a script to process customer data and generate insights"
Try:
"Write a function to load and validate customer CSV data"
"Write a function to clean customer names and addresses"
"Write a function to calculate customer lifetime value"
"Write a function to generate a summary report"
This approach has multiple benefits:
Each function is testable in isolation
You can review and understand each piece
Debugging is infinitely easier
You can reuse components across projects
AI focuses on doing one thing well instead of many things poorly
Example Pattern:
# AI's default approach (the "god function"):
def process_customer_data(file_path):
# 150 lines of data loading, validation, transformation,
# analysis, reporting, and file output all in one function
# Modular approach: def load_customer_data(file_path):
# Single responsibility: load and basic validation
def clean_customer_data(raw_data):
# Single responsibility: data cleaning
def analyze_customers(clean_data):
# Single responsibility: business logic
def generate_report(analysis_results):
# Single responsibility: output formatting
Define the interface first, then ask AI to implement each piece. This keeps the functions focused and prevents AI from solving world hunger inside your data processing loop.
Rule 4: Enforce Safety — Never Trust, Always Verify
Never auto-run AI code. Always review, test in isolation, and commit frequently. AI delivers broken code with the same confidence as perfect solutions.
I learned this during a client project where AI's "tested and optimized" data pipeline was silently returning fake results whenever real calculations failed.
The Non-Negotiable Safety Rules:
Never enable auto-run features. Tools like Cursor and GitHub Copilot offer tempting "apply all suggestions" buttons. Resist. Every line of AI code should pass through human review before execution. The time you save on review, you'll lose tenfold in debugging mysterious failures.
Test in isolation. Create a separate environment for testing AI-generated code. Don't run new AI functions directly in your main codebase. I use a simple testing pattern:
# Create isolated test environment
test_data = load_sample_data()
result = ai_generated_function(test_data)
print(f"Expected: {expected_result}")
print(f"Actual: {result}")
print(f"Match: {result == expected_result}")
Commit frequently. Before trying any AI suggestion, commit your working code. This creates restore points when AI decides your simple function needs message queues for "better scalability.”
Tag AI-generated commits clearly. Version control is your safety net. Many developers use an [AI] prefix in commit messages. This makes it easier to trace back and roll back when something subtle breaks later. Your future debugging self will thank you.
Rule 5: Set Context Clearly — Talk to AI Like a Contractor
Be ruthlessly specific about scope, constraints, and success criteria. AI takes everything literally and optimizes for the wrong things without clear boundaries.
AI responds well to explicit constraints and poorly to ambiguous requests. Treat it like you're briefing a contractor who takes everything literally.
Bad prompt: "Make this code better"
Good prompt: "Optimize this function for speed. Requirements: must handle 1M+ rows, keep the same input/output interface, no external dependencies, must complete in under 30 seconds."
The difference is specificity. The first prompt invites AI to reimagine your entire approach. The second gives clear boundaries and success criteria.
Context Templates I Use:
For quick analysis scripts: "Simple script for one-time analysis. No classes, no APIs, no error handling beyond basic try/catch. Priority is speed of development over code quality."
For production systems: "Production code that will be maintained by a team. Full error handling, logging, documentation required. Performance and reliability are critical."
For prototypes: "Prototype for stakeholder demo. Need it to work reliably for presentation, but doesn't need production-level optimization. Focus on clear, readable code."
The key insight: AI doesn't know your context unless you explicitly provide it. The same request for "data validation" should produce very different solutions depending on whether you're exploring a dataset or building a customer-facing system.
Security requires explicit prompting. AI doesn't think about injection attacks, secrets handling, or attack surfaces unless you force it to. Always specify security requirements upfront; don't assume AI will consider them automatically.
Rule 6: Cross-Validate Across AIs — Get Second Opinions for Critical Logic
For critical calculations, ask different AI models to solve the same problem using different approaches, then compare results. This catches errors that any single model might miss.
Here's a technique I bet most developers aren't using: when I need to validate critical calculations or complex logic, I ask different AI models to solve the same problem using completely different approaches, then compare results.
The Process:
Ask ChatGPT to implement the logic one way
Ask Claude to solve it with a different approach
Maybe get GitHub Copilot's take using different libraries
Compare all results with known test cases
Example—Calculating compound interest:
ChatGPT might use the standard mathematical formula
Claude might implement it as iterative calculations
Copilot might suggest using a financial library
When all three approaches give the same answer for your test cases, confidence increases significantly. When they diverge, you know to dig deeper.
This technique works like getting second opinions from different doctors. Each AI model has different training data, biases, and problem-solving patterns. The cross-validation catches errors that any single model might miss.
Real Example: I was implementing a customer segmentation algorithm and got three different approaches: Testing all three revealed that o e model was incorrectly handling missing data, The second approach was too slow for large datasets, but the third combined the best aspects of both. Without the comparison, I might have shipped the first solution.
Rule 7: Clean Up the Chatter — Strip AI's Comment Bloat
AI over-explains everything with verbose comments. Strip the bloat and keep only what helps future you understand the business logic.
AI loves explaining things. A lot. It will document every variable, explain obvious operations, and add commentary that would make sense in a computer science textbook but clutters real-world code. I’ll often ask it to update comments, “eliminating historical perspectives and buzzwords; discuss current functionality only in plain English.”
The goal is comments that help future you understand the why, not the what. Keep AI's insights about business logic or complex algorithms. Strip the tutorial-level explanations of basic operations (unless you are using this as a training exercise).
My comment cleanup checklist:
Remove comments that just restate the code
Keep comments that explain business logic or non-obvious decisions
Delete redundant explanations of standard library functions
Preserve warnings about edge cases or performance considerations
Rule 8: Enforce Quality Gates — Clear Contracts for Every Function
Every AI function needs a clear input/output contract. If you can't explain what it does in one sentence, break it down further.
Every function AI writes should pass a simple test: if you can't explain what it does in one sentence, it's doing too much.
Quality Gates:
Clear input/output contract: What goes in, what comes out, what format
Single responsibility: One job, one function
No mystery variables: Every variable name should be self-explanatory
Explicit error handling: What happens when things go wrong?
A good contract tells you everything: inputs, outputs, assumptions, and failure modes. If AI can't produce this level of clarity, the function needs refactoring.
The Meta-Skill: Managing AI Like Code Review
The real breakthrough came when I started treating AI interactions like managing a junior developer through code review. Every AI suggestion gets evaluated with the same questions I'd ask in a team code review:
Technical Questions:
What problem is this actually solving?
Is this the simplest solution that works?
What are the failure modes?
How will future maintainers understand this?
Business Questions:
Does this match our requirements?
Are we over-engineering for the use case?
What are the performance implications?
How does this fit into the bigger system?
Process Questions:
Is this testable?
What documentation does it need?
Are we introducing unnecessary dependencies?
How will we debug this when it breaks?
This mental framework transforms AI from a magic code generator into a collaborative team member whose work needs review and guidance.
Practical Implementation: A Day in the AI-Assisted Life
Here's how these rules play out in a real coding session:
9:00 AM - Problem Definition
I need to build a customer churn prediction model for a client demo.
Instead of: "Build a churn prediction system" I prompt: "I need a function that takes customer data and returns churn probability. This is for a prototype demo, not production. Focus on clear code over optimization. Use standard libraries, no deep learning frameworks."
9:15 AM - Architecture Planning
AI suggests a comprehensive ML pipeline with feature engineering, model selection, hyperparameter tuning, and deployment infrastructure.
My response: "That's too much. I need four simple functions: load_data, prepare_features, train_model, predict_churn. Implement them one at a time."
9:30 AM - Implementation
AI writes the first function. Before running it, I review:
Does it match my requirements?
Are there any surprise dependencies?
Is the error handling appropriate for a prototype?
9:45 AM - Testing
I test the function in isolation with sample data before integrating it.
10:00 AM - Cross-Validation
For the prediction logic, I ask another GenAI to implement the same algorithm differently and compare results.
10:30 AM - Documentation Cleanup
I strip out AI's verbose comments and add concise explanations of the business logic.
This workflow takes longer upfront but produces code I can actually maintain and debug.
When AI Gets It Right vs When It Goes Wrong
AI Excels At:
Boilerplate code generation
Standard algorithm implementations
Error handling patterns
Documentation templates
Code formatting and style consistency
Identifying edge cases you might miss
AI Struggles With:
Understanding business context
Making appropriate complexity trade-offs
Knowing when "good enough" is actually good
Integrating with existing systems
Performance optimization for specific use cases
Debugging its own generated code
The key is playing to AI's strengths while maintaining human oversight of its weaknesses.
The 8 Rules in One Sentence Each:
Stay the Boss: Never let AI design your system.
Question Everything: Stop improvisation before it hits code.
One Bite at a Time: Decompose aggressively.
Enforce Safety: Never trust, always verify.
Set Context Clearly: Scope like a contractor.
Cross-Validate Across AIs: Get second opinions.
Clean Up the Chatter: Strip comment bloat.
Enforce Quality Gates: Demand clear contracts.
Conclusion: Coding with AI Is About Managing Better, Not Typing Faster
After six months of daily AI coding, I've reached a counterintuitive conclusion: coding with AI is less about typing faster and more about managing better. The most valuable skill isn't learning to use AI…it's learning when to ignore it.
AI will always optimize for completeness and best practices. Your job is optimizing for the actual problem you're trying to solve. Sometimes that means accepting AI's enterprise-grade solution. Often it means ruthlessly editing down to the essential pieces.
The best developers in the AI era will be those who act like great tech leads: setting clear boundaries, reviewing work carefully, asking the right questions, and shipping only what matters. The same skills that help you guide junior developers will help you guide AI: clear communication, systematic review processes, and the ability to balance competing priorities.
These eight rules have transformed my relationship with AI from frustration to genuine productivity. I spend less time debugging mysterious AI decisions and more time solving actual business problems. The code I ship is simpler, more maintainable, and actually does what I intended.
The tool doesn't build everything. The tool helps you build only what's needed, but only if you manage it right.
Which of these rules resonates most with your workflow? Or do you have a different strategy that's saved you from AI chaos? Share your battle-tested rules in the comments.