AI coding assistants write boilerplate, tests and refactors in seconds. Used carelessly, they also produce plausible code that is wrong, insecure or inconsistent with your project. Teams that get lasting value treat the assistant as a fast junior developer: helpful, tireless and always in need of review. These are the habits we use.
Where assistants help most
- Boilerplate such as forms, types, API clients and configuration.
- First drafts of unit tests for existing functions.
- Explaining unfamiliar code or an error message.
- Mechanical refactors across many files.
- Drafting documentation and commit messages from a diff.
Where they need extra care
- Authentication, payments and anything handling personal data.
- Database migrations and destructive scripts.
- Concurrency, caching and performance-sensitive code.
- Libraries released after the model's training data, where it may invent functions that do not exist.
Give the assistant real context
Output quality follows input quality. State the framework and version, point to a similar file to copy the style from and describe the expected behavior and edge cases. A vague request such as "add login" returns generic code. "Add email and password login to this Next.js route using our existing session helper, return 401 on failure" returns something you can use.
Work in small steps
Ask for one function or one change at a time. Small diffs are easy to read and easy to reject. A 600-line generated change is one nobody reviews properly, and that is where bugs slip through.
Review every line as if a stranger wrote it
- Read the diff before running it and make sure you understand each part.
- Check that imported packages and functions exist and are actually needed.
- Look for hardcoded secrets, missing input validation and unsafe queries.
- Confirm error handling and the empty, loading and failure states.
- Run the code, not just the tests the assistant wrote for itself.
Keep humans responsible for tests
Assistant-written tests often assert what the code does rather than what it should do, so they pass even when the logic is wrong. Write the key test cases yourself from the requirements, then let the assistant fill in the rest. Keep coverage on critical paths and add a regression test for every bug.
Automate the guardrails
- Run a linter, formatter and type checker on every commit.
- Run tests and a dependency audit in CI, and block merges when they fail.
- Add secret scanning so keys never reach the repository.
- Require a human pull request review for every change, regardless of who or what wrote it.
Protect your data
Know what the tool sends to a provider. Do not paste customer data, private keys or confidential client code into services whose terms you have not read. Use business plans with data controls where they are available and keep a written policy for the team.
Keep your own skills sharp
Juniors who accept every suggestion stop learning how things work. Ask the assistant to explain its choices, try the problem first on your own and read the documentation for anything new. The goal is to move faster while understanding more, not less.
A simple team policy
- Assistants are allowed for routine code and tests.
- Sensitive areas need extra review from a senior developer.
- Every pull request is reviewed by a person who understands it.
- No confidential data goes into unapproved tools.
A review example
An assistant is asked to write a function that applies a discount to an invoice total. It returns tidy code that passes its own test, but it rounds each line item separately, so the invoice total is a few paise away from what the accountant expects. A reviewer who knows the business rule catches it in a minute. This is the pattern to expect: the code reads well and the mistake hides in the domain detail. Keep a written list of such rules, such as rounding, tax and stock adjustments, and give it to the assistant as context.
Prompts that work well
- "Write tests first for these three cases, then the function."
- "List the edge cases you are not handling."
- "Explain why you chose this approach over the existing helper in this file."
- "Show only the diff, and do not touch other files."
Where AI helps our own products
We also build AI features, not only use them to write code. Our WhatsApp assistants read free-form customer messages, pull out the name, need and budget, and send a clean lead to the team, as in our WhatsApp to Excel automation. In business software such as the Lena Dena platform, the same approach can read supplier bills and fill in invoice fields so staff check instead of typing. In both cases a person confirms anything that touches money, which is the same review-first rule this article recommends for code.
We use AI tools inside a review-first process, so clients get speed without surprises. See how we work on our services page, browse our projects, or talk to us about your build.