AI Code Generation Best Practices for Developers
Write better code faster with AI assistants by following these proven best practices and workflow patterns.
Provide Context, Not Just Instructions
The most common failure mode is asking for code without showing the code you already have. A model that cannot see your stack, conventions, and existing patterns will produce generic code that fights your architecture.
Consider the difference between two prompts for the same task. Before: Write a function to process refunds. This yields plausible but generic code with invented error handling and made-up types. After: Write a TypeScript function for our Express API that processes refunds through our PaymentGateway wrapper. Follow the structure of refundService.ts below, raise failures through our AppError class, and validate input using the Zod schema I pasted. The second prompt, with the referenced files actually included, produces code that drops into your codebase with minimal rework.
Practical habits that make this cheap:
- Paste one representative existing file whenever you ask for a new module, so the model can mirror naming and structure. - Name your stack and versions explicitly — framework, database, runtime — because defaults differ across versions. - State your error-handling and logging conventions once per session instead of fixing them in review every time. - Describe the business rule, not just the function signature; edge cases live in the business rule.
Review Like It Came From a Junior Developer
AI-generated code compiles and looks idiomatic far more often than it is actually correct. Treat it exactly as you would a pull request from a capable but unsupervised junior developer: read every line, question every assumption, and run it before you trust it. Recurring bug classes worth checking deliberately:
- Off-by-one errors in loops, slices, and pagination boundaries. - Null and undefined handling, especially around optional fields in API responses. - Race conditions in async code: unawaited promises, shared mutable state, missing locks. - Subtly wrong API usage — a real method called with the wrong argument order, a deprecated overload, or a default that changed between versions. - Hardcoded values that should come from configuration.
A useful discipline is to write one sentence explaining what each generated function does before merging it. If you cannot, you have not reviewed it.
Hallucinated APIs and Nonexistent Packages
Models regularly invent methods, options, and even entire packages. The hallucinated names are dangerous precisely because they are plausible — often blends of two real libraries or a method that exists in a different framework. Before building on any unfamiliar API, verify it in the official documentation, and try the import in isolation before writing code around it.
There is also a genuine security angle. Attackers have published malicious packages under names that AI models commonly hallucinate, a variant of typosquatting sometimes called slopsquatting. Never install a package solely because an assistant suggested it. Check the registry page, the download count, the maintainer history, and ideally the source repository first. In commonly shared analyses of AI coding sessions, invented package names appear often enough that this check should be a fixed habit, not an occasional one.
Security Review Is Not Optional
Generated code frequently omits the unglamorous security work. Check these explicitly on every AI-assisted change:
- Injection: database queries should be parameterized, never string-concatenated; the same applies to shell commands. - Secrets: models happily hardcode API keys and connection strings from your prompt into source files. Keep secrets in environment variables or a secret manager, and never paste real credentials into a prompt. - Input validation: assume every boundary — HTTP handlers, queue consumers, file parsers — receives hostile input, and verify the generated code validates it. - Authorization: generated endpoints often check authentication but skip per-resource authorization.
Asking the model to review its own output for vulnerabilities is a worthwhile second pass and regularly catches real issues, but it is a supplement to human review and static analysis tools, not a replacement.
Refine Iteratively Instead of Regenerating
When output is mostly right, resist the urge to re-roll the whole thing. Regeneration discards everything that was already correct and introduces new, unreviewed variance. Instead, name the specific defect: Keep this function as is, but add a guard for an empty items array that returns an empty summary object. Build features stepwise — data model, then endpoint, then wiring — reviewing each stage so later steps rest on verified code rather than on unexamined output.
Write the Tests First
Test-driven development pairs unusually well with AI. Write the tests yourself — or generate them and review them hard — so they encode your actual requirements, then ask the model to implement code that passes. This inverts the trust problem: instead of auditing opaque logic line by line, you verify behavior against specifications you control.
Be cautious with tests generated after the code by the same model in the same session. They tend to test what the code does rather than what it should do, cheerfully asserting the very bugs you wanted to catch. At minimum, review generated tests for missing edge cases: empty inputs, boundary values, error paths, and concurrent access.
When Not to Use AI Code Generation
- Novel algorithms or niche domains with little public code to learn from — output quality drops sharply and hallucination rates rise. - Security-critical primitives such as cryptography or authentication flows: use vetted, audited libraries instead of generated implementations. - Code you are not qualified to evaluate. If you cannot judge whether the output is correct, you cannot review it, and unreviewed AI code is a liability. - Compliance-sensitive code where the provenance and licensing of every line may be questioned.
Humans Own the Architecture
Models optimize for a plausible local solution; they do not carry responsibility for your system's long-term shape. Decisions about service boundaries, data flow, dependency choices, and failure modes should be made by people who will live with the consequences. Use AI to explore options, pressure-test a design by asking for counterarguments, and implement within the structure you set — but keep the decision itself, and the accountability for it, human.
Adopted together, these practices turn AI code generation from a source of subtle defects into a genuine productivity multiplier: real speed gains on drafting and boilerplate, with correctness still guaranteed by the person who merges the code.