AI Coding Best Practices.
15 rules that senior developers follow when coding with AI. Avoiding common AI coding mistakes starts here — developed from real-world experience across thousands of developers and production codebases.
Context & Prompting
How you communicate with AI determines 80% of the output quality. Our prompt engineering guide for developers goes deeper on this topic.
Start with context, not instructions
Before telling AI what to do, tell it what it's working with. Share relevant types, interfaces, existing patterns, and constraints. A prompt that starts with "Here is the User model and the existing auth middleware..." produces dramatically better code than one that starts with "Write a login endpoint."
One task per prompt, always
Never ask AI to do multiple unrelated things in one prompt. "Add validation to the form, fix the API error, and update the tests" will produce mediocre results on all three. Instead, send three focused prompts. Each task gets the AI's full attention and context window.
Specify constraints explicitly
AI doesn't know your team's rules unless you state them. "No external dependencies," "Must work with Node 18," "Use the repository pattern we already have," "Keep functions under 30 lines." Constraints narrow the solution space and produce more appropriate code.
Include examples of your patterns
The most effective context is "here's how we already do this." Show AI an existing controller, test, or component and say "follow this pattern for X." Pattern matching is one of AI's strongest capabilities. Leverage it by providing the pattern you want it to match.
Use rules files to encode team standards
Every major AI tool supports rules files (.cursorrules, CLAUDE.md, .github/copilot-instructions.md). These persist your team's conventions across every interaction. Include coding style, architecture patterns, preferred libraries, and common pitfalls. Update them as your team learns what works.
Quality & Review
AI generates code fast. Your job is ensuring AI-generated code quality meets production standards.
Read every line before committing
Speed kills quality. If you can't explain what a line does, don't commit it. AI generates plausible-looking code that hides subtle bugs: incorrect boundary conditions, missing null checks, wrong operator precedence, and silent data loss. The 30 seconds you spend reading prevents hours of debugging later.
Test AI code more aggressively, not less
AI-generated code deserves more testing than hand-written code because you didn't write it, so you don't have the implicit understanding of how it works. Write tests for edge cases, error conditions, and boundary values. Ask AI to generate tests, then add the cases it missed.
Watch for hallucinated APIs and dependencies
AI invents function names, package names, and API endpoints that don't exist. It confidently uses version-specific features from future releases or deprecated APIs from past versions. Always verify that imported packages exist, API endpoints are real, and function signatures match the actual library documentation.
Check for over-engineering
AI loves abstraction. It will create factory patterns, strategy patterns, and dependency injection where a simple function would suffice. If the generated code has more infrastructure than logic, it's over-engineered. Simplify. YAGNI (You Aren't Gonna Need It) applies even more when AI generates architecture.
Verify error handling is real
AI generates try/catch blocks that look thorough but often catch generic exceptions, swallow errors silently, or log without handling. Check that error handling actually recovers from errors, provides useful messages, and doesn't mask failures. Empty catch blocks from AI are technical debt that compounds fast.
Workflow & Process
How to integrate AI into your development process sustainably. See our AI coding productivity guide for the full workflow.
Decompose before you prompt
Break features into small, well-defined tasks before involving AI. "Build the user dashboard" is too vague. "Create a React component that displays the user's recent orders in a sortable table with pagination" is actionable. The decomposition step is human work that makes AI work dramatically better.
Commit AI changes in small, reviewable chunks
Avoid large AI-generated PRs. Each commit should represent one logical change that's easy to review and safe to revert. If AI makes changes across 15 files, break it into 3-4 focused commits. This makes review manageable and rollback surgical.
Use different models for different tasks
Not every task needs the most expensive model. Use fast, cheap models (Sonnet, Gemini Flash) for autocomplete, boilerplate, and simple generation. Use powerful models (Opus, GPT-5.4) for architecture decisions, complex debugging, and multi-file refactoring. Match the model to the task complexity.
Keep a "failures" log
Track when AI fails and why. Common patterns emerge: specific types of tasks it consistently gets wrong, contexts where it hallucinates, or prompts that produce bad output. This log becomes your team's institutional knowledge about AI limitations and informs your rules files.
Never stop learning the fundamentals
AI amplifies your knowledge. If you understand design patterns, AI helps you implement them faster. If you don't understand them, AI generates patterns you can't evaluate. Invest in CS fundamentals, system design, and your language/framework knowledge alongside AI skills. The combination is what makes you exceptional.
Go Deeper Than Best Practices
These 15 rules are the starting point. Our course teaches the complete system behind them: why each practice works, how to implement them in real codebases, and the advanced techniques that compound these gains. 12 chapters of hands-on training that works with any of the best AI coding tools.
Get the Accelerator for $79.99Frequently Asked Questions
Context management. The single biggest factor in AI code quality is the context you provide. Too little context and AI generates incorrect code. Too much context and it gets confused. The skill of selecting exactly the right files, types, and specifications for each task is what separates productive AI-assisted developers from those who find AI tools frustrating.
Yes, without exception. Even if AI generates code that passes tests, read every line. AI introduces subtle bugs that tests don't catch: incorrect error handling, security vulnerabilities, performance issues, and wrong assumptions about business logic. Treat AI as a talented junior developer whose code always needs review before merging.
Three strategies: (1) Use rules files (.cursorrules, CLAUDE.md) to encode your patterns. (2) Always include examples of your existing code as context so AI matches your style. (3) Run linters and formatters on all generated code. Many teams add AI-specific linting rules to catch common AI code smells like unnecessary abstractions or inconsistent naming.
You can use AI to write security-sensitive code, but the review bar must be much higher. AI generates auth flows, encryption, and input sanitization that looks correct but may have subtle vulnerabilities. Always cross-reference AI-generated security code with OWASP guidelines and framework-specific security documentation. Consider dedicated security review for AI-generated auth and payment code.
Review AI-generated code with the same rigor as human-written code. Many teams don't distinguish between the two in reviews. Some teams annotate AI-assisted code in PR descriptions so reviewers know to check for common AI patterns: over-engineering, hallucinated APIs, and missing edge cases. The key is that the committer is responsible for all code, regardless of its origin.