By Ronnie Mbuqe
A compiler checks language rules and types; it does not know every business rule. Valid code can still select the wrong record or apply the wrong policy.
Trace a realistic request through identity, authorisation, validation and storage. Each boundary needs checks beyond whether the method builds.
How can AI help developers?
Where developers use it
AI can explain an unfamiliar function, draft unit tests, suggest edge cases, transform repetitive mappings and help turn a rough requirement into a first prototype.
What it cannot own
The developer still owns architecture, security, data privacy, business rules, dependency choices, failure handling and the decision to release the change.
AI tools like ChatGPT, Copilot, Gemini and Claude can help developers understand code, generate examples, find mistakes and explain errors. But they work best as assistants, not as replacements for careful thinking.
Follow one complete feature
From “add search” to production: where AI helps and where the developer takes over
Say a team wants to add article search to a learning website. The requirement sounds short, but the real feature crosses the page, JavaScript, an API, authentication, SQL, error handling and monitoring. AI can shorten several steps; it cannot discover every business rule or accept responsibility for the release.
- Clarify the problem. The developer defines what may be searched, who may save history, what happens when nothing matches and how private member data must be handled. AI can suggest questions, but the product owner and developer decide the actual requirement.
- Understand the existing system. Before generating code, the developer traces the current page, service and database flow. The Software Development overview explains how these layers cooperate, while What Is an API? follows communication between systems.
- Request a small draft. AI may propose a search method, parameterised SQL call or test cases. Giving it one bounded task makes the result easier to inspect than asking it to generate an entire application.
- Review before running. Check types, null handling, authentication, authorisation and destructive behaviour. A query that works can still expose another member's records. The guides to SQL and authentication explain why those checks matter.
- Test normal and failure paths. Try a useful search, an empty term, special characters, no matches, a database timeout and a user without permission. AI can draft tests, but the developer must know what correct behaviour means.
- Release safely. Keep secrets out of prompts, review dependencies, use source control and deploy through the team's normal process. If the AI service is unavailable, the core website should fail clearly instead of breaking unrelated member functions.
- Watch the real feature. Logs, response time, search quality, cost and user feedback reveal problems that a local demonstration cannot. Production learning becomes the next input to human decisions, not automatic permission for the model to rewrite the system.
The important distinction
AI can produce code; it does not know that the code is correct for this website. Correctness comes from requirements, architecture, tests, security review and evidence from the running system.
Why can generated code pass a happy-path example and still be unsafe?
Code assistance can accelerate drafting, but correctness depends on the full contract.
Trace the explanation
Review validation, authorisation, error handling and dependencies. Test the behaviour that matters rather than only checking whether the suggested code compiles.
Source and further reading: cheatsheetseries.owasp.org
AI as a coding assistant
AI can explain an unfamiliar function, translate a stack trace into possible causes, draft unit tests, suggest edge cases and turn repetitive mappings into a first implementation. It is especially useful when the developer can judge the result quickly because they already understand the language and the expected behaviour.
The request should stay bounded. “Show three reasons this parameterised SQL query may return no rows” is easier to evaluate than “build my whole database.” “Draft tests for this method and include null input” is safer than asking for an entire payment feature with no requirements.
AI also helps with learning. A developer can ask it to compare two approaches, explain why an authentication check belongs on the server or trace how an API request moves through an application. The goal is understanding that survives after the chat closes.
Why you must still verify
Generated code can compile and still be wrong. It may call a method that does not exist in the installed version, ignore a null value, expose another user's record, trust input from the browser or update several database rows when the requirement allowed one.
Verification therefore asks more than “does it run?” Check the requirement, read every changed line, compile it, test expected and failure paths, inspect database boundaries, review authentication and authorisation, scan dependencies and confirm that logs do not expose private data. High-risk areas such as payments, permissions, cryptography and destructive SQL deserve stronger review than a draft label or test-data helper.
Generated code can repeat a dangerous pattern with excellent formatting
An assistant may produce a query that concatenates user input or a method that trusts a client-supplied account key. The code can compile and look professional while violating the application's security boundary. Fluency in syntax is not proof of a safe design.
Give the tool a small, explicit contract and ask it to explain assumptions. Then inspect data access, permission checks and error paths yourself. Test a meaningful hostile or boundary case rather than merely running the sample input included in the generated answer.
State a narrow requirement to Verify against independent examples
Good use of AI
Good use leaves the developer more informed and the change easier to verify. Examples include generating test cases from known rules, explaining a small legacy method, creating sample data, drafting documentation from reviewed code or suggesting several approaches before the team chooses one.
Poor use hides missing understanding. Copying a large generated feature into production, pasting private source code into an unapproved service or executing an unfamiliar database script transfers speed into risk. The practical rule is simple: use AI where checking is cheaper than writing from the beginning, and slow down wherever a subtle error could harm users or data.
Practical examples: AI for Developers
Explaining
AI can translate a compiler message or stack trace into likely causes and suggest what evidence to inspect next. That is useful for orientation, but the developer confirms the failing line, runtime state and project-specific behaviour before accepting the explanation.
Generating
For a small, well-defined function, AI can produce a first draft and matching test cases quickly. The developer checks edge cases, naming, dependencies and whether the generated approach follows the existing architecture instead of creating a second pattern for the same job.
Writing
AI can help draft documentation, migration notes and examples from code that a developer is allowed to share. Private keys, personal data and confidential source should not be pasted into an external model merely because the task looks convenient.
Reviewing
AI can point out duplicated logic, missing null checks or suspicious flows during review. It cannot know every business rule or production constraint, so a human reviewer still verifies the change, reads the surrounding code and owns the decision to release it.
A generated function passes 7 of 10 specified scenarios. How many scenarios still fail?
Trace the explanation
10 − 7 = 3. Essential failures do not become acceptable because most examples pass. Investigate what each failing scenario represents before deciding whether the change is usable.
Source and further reading: cheatsheetseries.owasp.org
Why can a helpful coding assistant invent a library method?
Generated code can follow the naming patterns of an API without matching an actual installed version. Plausibility is not documentation.
Open the official reference for the package version, inspect the signature and run a relevant example. Treat a suggestion as a candidate implementation that needs verification.
AI can accelerate a draft, not accept responsibility
The safest uses have a clear requirement and a cheap way to verify the result. Developers remain responsible for project fit, compilation, tests, security, privacy, licensing, cost and production behaviour.
Tool, not autopilot
Where AI actually fits into software development
For developers, AI is most useful when it reduces mechanical work while the engineer remains responsible for the system. It can explain unfamiliar code, propose tests, draft repetitive mappings, suggest refactors and help search documentation. Those outputs still need review because a plausible-looking answer can misunderstand local business rules, use an outdated library or introduce a security problem.
AI for Developers in a real situation
Consider adding a new API endpoint to an established booking platform. An AI assistant can draft a controller method or unit-test skeleton quickly, but it cannot automatically know every rule hidden in the existing database procedures, permission bits or payment flow. A developer must compare the suggestion with the current architecture, run tests and check security before merging it. The speed gain comes from starting with a useful draft instead of handing over ownership of the decision.
What makes this useful
The strongest developer workflow treats AI like a fast collaborator with incomplete context. Give it a bounded task, provide the relevant constraints, inspect the output and verify the result with tools. Never place production secrets, customer data or confidential code into a service unless the organisation has approved how that data is handled.
A common point of confusion
AI coding tools do not remove the need to understand programming. In practice, generated code makes judgment more important because someone still has to recognise whether the solution is correct, maintainable and appropriate for the codebase.
Verification should cost less than doing the work from scratch
A generated regular expression for a narrow text format may be easy to test against examples. A generated payment workflow touches retries, financial records and uncertain responses, making verification much harder. Use task risk and inspectability to decide how much automation is appropriate.
For a production change, trace one input through validation, persistence and output. Confirm that the assistant used the project's actual library versions and interfaces. A plausible method name from another version is still a broken dependency. Keep confidential source and credentials out of tools unless your organisation's approved setup permits their use.
Is a test generated from the same wrong assumption an independent check?
No. The implementation and test may agree with each other while both contradict the requirement. Include examples grounded in the intended behaviour and actual integration contract.
A generated endpoint returns any booking by ID after login. What question should a reviewer ask immediately?
One reasoned response
Does it verify that this user is entitled to this booking? Authentication is not resource authorisation. Review the data boundary as well as the syntax and response shape.
Source and further reading: cheatsheetseries.owasp.org
Frequently Asked Questions
Questions about AI for Developers
Can AI replace developers?
AI can automate some tasks, but software still needs human judgement, business understanding And responsibility.
Can beginners use AI to learn coding?
Yes, but they should ask for explanations, not just answers. A beginner who copies a working code snippet without understanding why it works will struggle the moment the task changes slightly and that exact snippet no longer applies.
Can AI write a whole application?
It can generate substantial code, but production software also requires architecture, security, data design, testing, deployment and domain decisions that need accountable human review.
Should developers paste errors into AI tools?
Errors can be useful context, but first remove passwords, tokens, customer information and proprietary data that should not leave the approved environment.
What is a good use of AI in debugging?
Ask it to explain a stack trace, suggest hypotheses or compare two code paths, then verify those hypotheses with logs, tests and the actual runtime behaviour.
From Prototype To Production
A developer can make an AI demo look impressive in an afternoon: send a prompt, receive text and display it on a page. Production is different. The application has to decide what data may be sent to the model, what happens when the model is unavailable, how long a request may take, how costs are controlled and whether a human must approve the result. Those are software-design questions, not prompt-writing tricks.
Consider an AI feature that summarises a customer support ticket. The safe design does not let the model silently change the customer account. It produces a proposed summary, records which source text was used, validates the response format and lets normal application rules decide what actions are allowed. If the model returns nonsense, the core system still knows how to fail safely.
The useful mindset is to treat AI as an uncertain external dependency. Measure quality with real examples, log failures without leaking sensitive data, version prompts just as you version code, and keep deterministic business rules outside the model when correctness matters.
Sources
Sources and further reading
A closer lookGenerated code needs a failure case
An AI tool suggests a cancellation function that restores the entire reservation credit. The actual rule restores only the cancelled booking's allocation.
Work through the distinction
Write the invariant first: which amount belongs to this booking, what must remain unchanged and how a retry avoids a second restoration. Review the generated code against those rules and test a reservation with multiple bookings.
What should guide review of generated code?
Choose an answer to see feedback. You can try again.
Show the answer and explanation
The system's explicit invariants. Write the invariant first: which amount belongs to this booking, what must remain unchanged and how a retry avoids a second restoration. Review the generated code against those rules and test a reservation with multiple bookings.