AI in Software Development

01Where does AI actually help in the development cycle?

In real projects AI help is not concentrated in one spot; it is spread across the whole cycle. From clarifying requirements and early design to writing tests, fixing bugs and documenting, every stage has tasks a model can move forward.

• Code completion and generation: small functions, boilerplate, data transforms and throwaway scripts.
• Testing: suggesting edge cases and writing unit tests for code that already exists.
• Refactoring: renames, extracting functions and migrating from an old API to a new one across many files.
• Understanding unfamiliar code: summarising a legacy module or explaining a gnarly regular expression.
• Docs and commit messages: draft READMEs, function docs and pull-request summaries.

If you want a starting point, our guide to prompt engineering for developers shows how to write requests that return usable output.

02What to delegate and what to keep

A simple rule on our team: if checking the result is cheaper than writing it, it is a good candidate for delegation. The table below sums up our practical view; there are no numbers because they depend on the project and the model.

03A practical workflow with an AI assistant

The worst pattern is giving a vague request and pasting the result blindly. A better pattern is small and iterative:

1. State the goal and constraints: language, framework version, relevant files and what must not change.
2. Ask for a plan first, not code. If the plan is wrong, fix it here.
3. Take the change on a separate branch in small steps so the diff stays readable.
4. Run tests and the linter; feed failures back to the model.
5. Before merging, read the diff yourself as you would a junior colleague's code.

04Real risks: invention, leaks and security

Invented APIs and packages

Models sometimes confidently suggest a function or package that does not exist. Worse, an attacker can register a package under that very name. Before installing any new dependency, check that it exists and is healthy:

# Does the package exist, who maintains it, how old is it?
npm view some-suggested-package name version time.created maintainers

# Same idea for Python
pip index versions some-suggested-package

Leaking confidential data

API keys, database passwords and customer data should never go into prompts or cloud tools without a clear data-handling agreement. Read the tool's retention policy first and keep .env files out of the assistant's reach.

Security flaws

Generated code can skip input validation, build SQL by string concatenation or expose raw errors. These are the common mistakes catalogued by OWASP. Make security review stricter for model-written code, not looser; for authentication keep our guide to JWT authentication at hand.

05How to measure the real impact

The feeling of "we are faster" is deceptive: writing gets quicker, but review and debugging may give the time back. Record a few simple indicators before and after introducing a tool:

• Time from opening a pull request to merge, and the number of review rounds.
• Bugs reported after release, and the rollback share.
• Test coverage and quality, not just the count.
• Developer satisfaction from a short monthly survey.

If your service already has solid monitoring and logging, quality shifts after adopting a tool show up sooner; professional monitoring and logging covers that groundwork.

06A team-policy checklist

• Keep a list of approved tools and the kind of data each may receive.
• Check every model-suggested dependency before installing it.
• Generated code passes CI, tests and review like any other code, with no exceptions.
• Sensitive areas (payments, auth) need a human author and a second reviewer.
• Keep the team's useful prompts in a shared repository and maintain them.

If you want to use AI inside your product and not just your process, building a chatbot with LLMs is a hands-on example, and our AI service page explains how we work.