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Written by Jacob Sipos
Published Sep 22, 2026
Use AI for Coding
AI can make coding more approachable without making judgment optional.
You can use AI to explain code, create prototypes, find bugs, write tests, refactor messy files, document a project, search a codebase, review changes, and even delegate multi-step development tasks.
The best way to think about it is not “AI replaces programming.” It is AI gives you another collaborator at the keyboard—one that can work quickly, suggest ideas, and still needs your review.
// Create a simple greeting
function greet(name){
return `Hello ${name}!`;
}
greet( “Alex”);
function greet(name){
return `Hello ${name}!`;
}
greet( “Alex”);
Make this function handle an empty name.
AI suggestion
Add validation and a default response.
Add validation and a default response.
✓ change prepared · review before applying
First, What Does “Use AI for Coding” Actually Mean?
It means using an AI system as part of the software-development process. Depending on the tool, AI can suggest code while you type, answer questions about a project, edit files, run commands, generate tests, review pull requests, research a codebase, or take on a larger task as a coding agent.
Current coding assistants have moved beyond simple autocomplete. GitHub documents agent mode, code review, and cloud agents that can research repositories, create branches, make changes, and open pull requests for human review. OpenAI describes Codex as a coding agent for writing, reviewing, refactoring, migrating, and completing engineering tasks.
You have an idea for a small website but do not know how to create the navigation. AI can explain the options and generate a starting version.
You inherit a 2,000-line file and do not understand what it does. AI can summarize the structure and point you toward important functions.
A test fails at 4:30 PM. AI can inspect the error message, suggest likely causes, and help you investigate.
The Important Distinction
AI can produce code very quickly. Producing code and producing correct, secure, maintainable software are not the same thing. The value comes from pairing AI speed with human understanding, testing, and review.
You Do Not Have to Be an Expert Programmer to Benefit
DESCRIBE
SEE
ASK
TEST
LEARN
AI can lower the barrier to experimenting with code because you can describe what you want in ordinary language.
If you are new to coding
Use AI as a tutor. Ask what each line does, why an error occurred, what alternatives exist, and how to test your understanding.
If you already code
Use AI to accelerate repetitive work, explore unfamiliar libraries, generate tests, inspect large codebases, draft migrations, and review changes.
The strongest beginner workflow is not “give me an entire application and hope it works.” It is build something small, understand it, test it, then add the next piece.
Coding Prompts Work Better When They Sound Like a Good Work Order
Give the AI enough context to make a useful decision.
Build a responsive pricing table in plain HTML and CSS. Use three plans, stack them vertically below 700px, avoid external libraries, and explain the structure after the code.
GOAL
TECHNOLOGY
CONSTRAINTS
EXPECTED OUTPUT
EXPLANATION
A vague prompt such as “make this better” forces the AI to guess what “better” means.
State the outcome.
What should the user be able to do when the work is complete?
State the environment.
Language, framework, version, database, operating environment, and relevant project conventions matter.
State the constraints.
No new dependencies? Must support older browsers? Must preserve an API? Say so.
Ask for verification.
Request tests, edge cases, assumptions, and a short explanation of the changes.
Useful pattern: “Before changing anything, explain your plan and identify the files you expect to touch.” That makes a larger coding task easier to review before code starts moving.
AI Can Turn Code Into Plain English
CODE
const total = items.reduce( (sum,item) => sum + item.price, 0 );
→
PLAIN ENGLISH
Go through every item, add its price to a running total, and start that total at zero.
This is one of the safest and most useful ways to learn with AI. Ask it to explain a function line by line. Ask why a particular data structure is being used. Ask for a simpler example. Ask what would happen if the input were empty.
You copy a regular expression from an old project and nobody remembers what it does.
Instead of treating it as magic, ask AI to explain each part, provide matching and non-matching examples, and suggest a clearer alternative if one exists.
Then test the explanation. AI can sound confident while being wrong, especially when code depends on an unfamiliar library, undocumented behavior, or a specific version.
Go From Idea to Working Prototype Much Faster
AI is particularly good at helping create first versions of familiar patterns: forms, APIs, dashboards, scripts, data transformations, tests, command-line tools, layouts, and repetitive application structure.
IDEA
PLAN
PROTOTYPE
RUN
REFINE
You want a tiny internal page where a user pastes CSV data and receives a formatted summary.
AI can help sketch the interface, write the parsing logic, create sample data, explain how to run it locally, and generate tests. You can get to the point where you are evaluating a real prototype instead of discussing an abstract idea.
This is powerful because working software exposes questions that planning alone may not reveal.
But prototypes accumulate risk when they quietly become production systems without appropriate architecture, testing, security review, accessibility review, performance work, and maintenance planning.
Debugging With AI Is Like Having Someone Ask, “What Changed?”
A useful debugging conversation
TypeError: Cannot read properties of undefined
at renderProfile (profile.js:42)
AI hypothesis: user.profile may be undefined before the request completes.
at renderProfile (profile.js:42)
AI hypothesis: user.profile may be undefined before the request completes.
1 · Inspect the value at the failing line
2 · Reproduce with a missing profile
3 · Add a guard or loading state
4 · Add a test so it does not return
Instead of pasting an error and asking “fix this,” provide the relevant code, error message, what you expected, what happened, and what changed recently.
Ask for several plausible causes ranked by likelihood. That encourages investigation rather than jumping straight to one confident answer.
Excellent debugging prompt: “Do not patch the symptom yet. Explain the likely root cause and show me how to prove or disprove it.”
AI Can Write Tests You Might Not Think to Write
Expected behavior
Normal input
PASS
Empty input
PASS
Duplicate value
PASS
Edge cases
Huge input
PASS
Malformed date
FAIL
Missing field
PASS
AI can generate unit tests, integration-test ideas, mock data, edge cases, and failure scenarios from existing code.
That is valuable because humans naturally test the path they expect. An AI assistant can quickly suggest the empty list, zero value, missing field, unusual Unicode character, expired token, duplicate request, huge payload, or timing problem you forgot.
Do not measure test quality by the number of tests. A generated test that merely mirrors the implementation can pass while the product behavior is still wrong.
Use AI to Clean Up Code Without Changing What It Does
Refactoring means improving code structure while preserving behavior. AI can help rename confusing variables, split oversized functions, reduce duplication, modernize patterns, add types, explain dependencies, and draft documentation.
Before the refactor
Ask AI to summarize current behavior, assumptions, side effects, and existing tests.
After the refactor
Run the tests, inspect the diff, and verify externally visible behavior did not change unexpectedly.
For legacy systems, AI can also be useful simply as a map. Ask: “Which functions write to the database?” “Where is authentication checked?” “What calls this method?” “Which files would be affected if this API changes?”
The Big Shift: AI Can Now Work Across the Project, Not Just Answer One Question
CODING AGENT · REPOSITORY CONTEXT
▾ src
app.ts
auth.ts
profile.ts
▾ tests
profile.test.ts
Add profile photo validation and tests. Do not change the public API.
1 · Read relevant files
2 · Plan the change
3 · Edit implementation
4 · Run tests
5 · Present diff for review
Modern coding agents can inspect multiple files, make edits, execute commands, run tests, research a repository, and work through multi-step tasks. GitHub documents cloud agents that can create branches and pull requests, while OpenAI’s current Codex materials describe end-to-end engineering tasks, refactors, migrations, and parallel agent workflows.
This is a major productivity shift, but it also changes the review burden. When an assistant changes 14 files instead of suggesting 8 lines, you need a process for understanding the plan, reviewing the diff, running tests, and controlling permissions.
Delegate the Work, Not the Accountability
Treat an agent’s pull request like a contribution from a very fast teammate. Read it, test it, challenge its assumptions, request changes, and merge only when you understand what you are accepting.
AI Can Review Code Too—Including Code Another AI Wrote
PULL REQUEST · CODE REVIEW
41
42
43
44
42
43
44
– return user.name.toLowerCase()
+ return user?.name?.toLowerCase() ?? “”
Review note: This prevents the crash, but confirm whether an empty string is the correct business behavior when the user is missing.
GitHub currently offers Copilot code review that can analyze pull requests, identify issues, use repository context, and suggest fixes. GitHub’s own guidance emphasizes that Copilot pull requests should receive the same thorough review as other contributions.
AI review can be useful as an extra pass for bugs, maintainability, missing tests, suspicious logic, or overlooked cases. It should complement rather than replace the review practices appropriate to your project.
The Faster Code Appears, the More Important Verification Becomes
✓
Check authentication and authorization
Validate and sanitize untrusted input
Protect secrets and credentials
Review dependencies and licenses
Run tests and security tooling
Understand data sent to AI services
AI can accidentally suggest insecure patterns, outdated APIs, invented package names, overly broad permissions, vulnerable dependency versions, or logic that looks reasonable but fails under hostile input.
This does not make AI uniquely unusable. Human-written software has bugs too. It means AI belongs inside a software-engineering process with tests, code review, security controls, version control, dependency checks, and appropriate access restrictions.
AI writes a convenient database query that builds SQL by concatenating user input.
The feature appears to work perfectly during a normal demo. The problem only appears when someone sends malicious input. Correctness includes what happens when the user does something unexpected or adversarial.
Never paste secrets, private keys, production credentials, sensitive customer data, proprietary code, or regulated information into a coding service unless its approved data handling and your organization’s policies permit it.
How to Use AI for Coding More Efficiently
Ask for a plan before a large change.
Review the proposed approach before the assistant edits a large part of the codebase.
Keep tasks small enough to review.
“Fix this validation rule and add tests” is easier to evaluate than “improve the application.”
Give the AI project rules.
Architecture, naming, style, supported versions, testing requirements, and forbidden dependencies reduce unnecessary guessing.
Ask for tests with the implementation.
Make validation part of the task instead of something added after the code appears.
Use version control.
Small commits and clear diffs make AI-assisted experiments safer and easier to reverse.
Make the AI explain surprising code.
If you cannot understand why a change works, do not let speed pressure you into accepting it.
Automate routine work, protect consequential decisions.
Boilerplate, tests, documentation, search, formatting, and repetitive refactors are excellent candidates for assistance. Security architecture and business-critical behavior deserve deeper scrutiny.
Things People Often Do Not Realize AI Can Do for Coding
Explain an unfamiliar repository before you touch it.
Ask for architecture, entry points, data flow, dependencies, risky areas, and the likely files involved in a proposed change.
Generate tests from existing behavior.
AI can inspect functions and suggest normal cases, edge cases, failure cases, and missing coverage.
Turn an error log into an investigation plan.
Instead of one guessed fix, ask for several hypotheses and the fastest evidence that would distinguish between them.
Create migrations and repetitive refactors across many files.
Current coding agents are designed for multi-file, multi-step work such as migrations and complex refactors, subject to tool permissions and review.
Review pull requests and suggest fixes.
Modern coding assistants can perform code-review passes using repository context and propose changes for human evaluation.
Run commands and tests on your behalf.
Agentic tools can use terminals or managed environments to install dependencies, run tests, inspect failures, and iterate where permissions allow.
Write documentation after reading the implementation.
AI can draft README sections, API explanations, inline comments, changelogs, onboarding notes, and architecture summaries from project context.
Work on several engineering tasks in parallel.
Some current agent platforms support multiple isolated workstreams so separate tasks can progress concurrently and return changes for review.
AI for Coding Is a Tool. Good Engineering Makes the Tool Valuable.
| Benefit | Helpful Practice |
|---|---|
| AI can generate working code very quickly. | Understand the behavior, run it, test it, and inspect the diff before treating it as finished. |
| AI can make coding more approachable for beginners. | Ask for explanations and build incrementally so you learn rather than accumulating code you cannot maintain. |
| AI can inspect large codebases and automate repetitive work. | Limit permissions appropriately and keep tasks narrow enough for meaningful review. |
| AI can debug and propose fixes. | Confirm the root cause. A patch that hides the error may create a deeper problem later. |
| AI can generate tests and perform code review. | Use independent checks too. AI reviewing AI-generated code is still not the same as proof of correctness. |
| Coding agents can take multi-step actions. | Use branches, sandboxes, version control, tests, approval gates, and least-privilege access. |
| AI can save substantial development time. | Follow privacy, security, licensing, intellectual-property, and organizational rules for the code and data provided to the service. |
A 7 Day AI Coding Challenge
Day 1: Ask AI to explain code.
Choose a small function and ask for a line-by-line explanation plus examples.
Day 2: Build something tiny.
Create a calculator, form validator, small webpage, or script. Run it yourself and understand every major piece.
Day 3: Debug deliberately.
Give AI an error and ask for hypotheses before asking for a fix.
Day 4: Generate tests.
Ask for edge cases, run the tests, and see whether any reveal a real problem.
Day 5: Refactor one messy function.
Ask for a plan, preserve behavior, and compare the before-and-after code.
Day 6: Review a change.
Ask AI to critique code for bugs, readability, tests, and security, then perform your own review too.
Day 7: Try an agentic workflow.
On an appropriate test project, delegate one bounded multi-file task and review every changed file before accepting it.
Final Thoughts
AI is changing coding because it reduces the distance between an idea and a working first version.
You can describe what you want instead of remembering every syntax detail. You can ask why unfamiliar code works. You can turn an error into an investigation. You can generate tests, refactor repetitive code, search a large repository, create documentation, review a pull request, and delegate multi-step tasks to an agent.
That is extremely valuable—but the part that makes software dependable has not disappeared.
You still need to know what the program is supposed to do. You still need tests. You still need security. You still need version control. You still need to understand important changes before they reach users.
The best AI coding workflow is not “ask for code and trust it.” It is “describe, inspect, run, test, question, review, and improve.” AI can make every one of those steps faster while you remain responsible for what ultimately ships.
Disclaimer: This article provides general educational and informational guidance about AI-assisted software development. Coding tools, models, agents, plans, permissions, integrations, security controls, and capabilities change frequently and can vary by provider, account, environment, administrator settings, and product rollout. AI-generated code may contain bugs, security vulnerabilities, licensing issues, outdated patterns, or incorrect assumptions and should be reviewed, tested, and validated appropriately before use. Follow applicable organizational policies, software licenses, privacy requirements, security standards, intellectual-property obligations, and regulatory requirements. For consequential security, legal, privacy, compliance, or production architecture decisions, involve appropriately qualified professionals. Product names mentioned are trademarks of their respective owners. This independently created educational article is not affiliated with or endorsed by any AI coding provider. All decorative editor, terminal, repository, review, and security graphics on this page are original HTML and CSS and do not use copied product screenshots, stock imagery, or watermarked artwork.
Official references: GitHub Copilot · GitHub Copilot Agents · OpenAI Codex
