Setting AI Coding Standards and Guidelines for Your Team

Why You Need Standards Before You Need Enthusiasm

Every team I've seen rush into AI adoption without standards ends up in the same place six months later: inconsistent code quality, no clear process for reviewing AI-generated code, and a growing sense that "AI is making our codebase worse."

It's not that AI is making it worse. It's that without guidelines, ten different developers using AI in ten different ways produce chaos. The same way coding standards and style guides transformed how teams maintain code quality, AI coding standards are necessary to maintain that quality in the AI era.

This isn't about restricting how people use AI. It's about ensuring that AI usage improves your codebase rather than fragmenting it.

The AI Coding Standards Framework

After implementing AI guidelines across multiple teams, I've developed a framework with six categories. Each category addresses a different aspect of AI-assisted development.

Category 1: Acceptable Use

Define clearly where AI is encouraged, permitted, and prohibited.

Encouraged (actively recommended):

• Writing unit tests and integration tests
• Generating boilerplate code (component scaffolding, API route stubs, database schemas)
• Creating documentation (JSDoc, README sections, API docs)
• Code refactoring (improving existing code structure)
• Writing commit messages and PR descriptions
• Generating mock data for development and testing

Permitted (use with caution):

• Implementing business logic (must be thoroughly reviewed)
• Creating UI components (must follow design system)
• Database queries (must be reviewed for performance and security)
• API integrations (must verify against actual API docs)

Prohibited (human-only):

• Authentication and authorization logic
• Encryption and security-critical code
• Payment processing flows
• Production deployment scripts
• Incident response and debugging (AI can research, not decide)
• Code that handles PII or sensitive data without explicit review

This isn't a permanent list — it evolves as the team gains confidence and tools improve. But starting with clear boundaries prevents costly mistakes.

Category 2: Code Review Requirements

AI-generated code must meet the same quality bar as human code. But the review process should account for AI-specific risks.

Rule 1: Disclosure. Developers should indicate when significant portions of a PR were AI-generated. This isn't about blame — it's about directing reviewer attention. A comment like "AI-assisted: the test file and the utility functions. Business logic written manually." gives reviewers context.

Rule 2: Understanding. Every developer must be able to explain every line of code they submit, regardless of origin. If a reviewer asks "why did you implement it this way?" and the answer is "the AI suggested it," that's a failing review.

Rule 3: AI-specific review checklist. In addition to your normal review checklist, add:

• Verify all imports exist in the project's dependency tree
• Confirm API calls match your actual API contracts
• Check for hallucinated function names or methods
• Validate that error handling matches your application's error strategy
• Ensure consistent naming with the rest of the codebase

Rule 4: Extra scrutiny thresholds. Any AI-generated code that touches data persistence, external API calls, or user-facing error messages gets an additional reviewer.

Category 3: Testing Requirements

AI-generated code requires specific testing approaches:

Mandatory for AI-generated code:

• Unit tests for all business logic (no exceptions)
• Integration tests for any code that interacts with external systems
• Manual testing for UI components (AI can miss visual/UX issues)
• Edge case tests explicitly listed and verified (AI often misses domain-specific edge cases)

Test quality standards:

• Tests must be meaningful, not just coverage-padding
• AI-generated tests must be reviewed for false positives (tests that pass for the wrong reason)
• Property-based testing encouraged for complex transformations
• Snapshot testing alone is insufficient for AI-generated UI code

Category 4: Documentation Standards

AI makes documentation easier to generate but doesn't guarantee it's useful. Standards for AI-assisted documentation:

Code-level documentation:

• JSDoc for all public functions and exported types
• Comments explaining "why," not "what" (AI often generates "what" comments that add no value)
• README updates when AI generates new modules or utilities
• Architecture Decision Records (ADRs) for significant AI-assisted design choices

AI interaction documentation:

• Save effective prompts in a team wiki for common tasks
• Document patterns where AI consistently produces poor results
• Share "gotcha" findings (e.g., "AI always uses deprecated API X; use Y instead")

Category 5: Prompt Standards

Consistency in how the team interacts with AI improves output quality:

Context setting:

• Always provide relevant type definitions when asking AI to generate TypeScript
• Include your linting rules or link to your .eslintrc configuration
• Specify your target environment (Node version, browser support, etc.)
• Reference existing patterns: "follow the same pattern as [specific file]"

Quality instructions:

• Specify error handling expectations
• Request that AI follows your naming conventions
• Ask for edge case handling explicitly
• Request that AI explains its decisions, not just generates code

Anti-patterns to avoid:

• Don't copy-paste entire files into prompts without context
• Don't ask AI to "make this work" without specifying what "work" means
• Don't accept the first response — iterate and refine
• Don't use AI for one-liners that you can write faster yourself

Category 6: Version Control and Attribution

How AI-assisted code should be handled in version control:

Commit practices:

• AI-assisted commits follow the same commit message standards as all other code
• Large AI-generated additions should be in separate commits from human-written logic
• Don't commit AI-generated code without reviewing and testing first (sounds obvious, but it happens)

Branch strategy:

• AI-heavy feature work should go through the same PR process
• Consider smaller, more frequent PRs when using AI (easier to review incremental AI code than a massive AI-generated PR)

Implementation Guide

Week 1: Draft and Circulate

Draft the initial standards document. Keep it concise — one page, not ten. Circulate it for feedback. The goal is consensus, not a mandate.

Week 2: Trial Period

Adopt the standards as "guidelines" for two weeks. Encourage the team to follow them and provide feedback on what's practical and what's overly restrictive.

Week 3: Revise

Based on feedback, revise the standards. You'll find that some rules are too strict (nobody follows them) and some are too loose (issues are slipping through).

Week 4: Formalize

Formalize the standards. Add them to your team wiki or engineering handbook. Include them in your onboarding documentation.

Ongoing: Quarterly Review

AI tools evolve rapidly. Standards that make sense today might be outdated in three months. Schedule a quarterly review to update your guidelines based on new tools, new capabilities, and team experience.

The Living Document Principle

The most important thing about AI coding standards is that they're a living document. They should evolve as:

• AI tools improve (what's prohibited today might be permitted tomorrow)
• Your team's AI literacy increases (junior teams need stricter guidelines)
• New risks emerge (new attack vectors, new failure modes)
• Industry best practices develop (we're all learning together)

Don't treat them as carved in stone. Treat them as a conversation that's always ongoing.

A Note on Trust

The purpose of AI coding standards isn't to distrust your developers. It's to create shared expectations. Just like code style guides aren't about distrusting people's taste — they're about consistency.

When everyone knows the rules, there's less friction in code reviews, less ambiguity about what's expected, and more confidence in the codebase.

Standards aren't the enemy of innovation. They're the foundation that makes innovation sustainable.