AI coding tools such as Cursor and Claude Code help web teams write, change and review website code by working directly with the project files. They save the most time on well-scoped tasks with clear checks, such as fixing a template bug, adding a component, writing a migration script or auditing pages for accessibility, and they still need a developer to review every change before it goes live.
What are Cursor and Claude Code?
Cursor is a code editor built on the same base as Visual Studio Code, with AI features for chat, inline edits and an agent mode that can change several files at once. It can use models from several providers.
Claude Code is Anthropic’s command-line coding tool. It runs in a terminal or inside editors, reads the project, runs commands and edits files with the developer’s approval. Teams often add a project notes file, such as CLAUDE.md, that tells the tool about conventions and fragile parts of the codebase.
Other tools in the same space include GitHub Copilot, Windsurf and OpenAI’s Codex. The workflows below apply to most of them.
Workflow 1: How do you fix a front-end bug faster?
Bug fixing is where these tools often pay off first, because the task has a clear end: the bug is gone and nothing else broke.
- Write the bug as a short report: page, steps to reproduce, expected and actual result
- Give the tool the report and ask it to find the cause before changing anything
- Review its explanation; if it is wrong, correct it early
- Let it propose a fix limited to the files involved
- Run the site locally and check the page on mobile and desktop widths
- Commit with a message that explains the cause, not just the fix
Asking for the cause first stops the tool from patching symptoms. A wrong theory caught at step three saves an hour of reverts later.
Workflow 2: How do you add a new component?
Adding a pricing table, testimonial slider or FAQ block is routine work with many small decisions about naming, styling and structure.
Steps
- Point the tool to an existing component that follows house style
- Describe the new component: content fields, layout on mobile and desktop, accessibility needs
- Ask it to follow the same file structure and CSS naming
- Review the markup for heading order, labels and keyboard support
- Test with real content, including long text and missing images
In a WordPress block theme, for example, this might mean a new block pattern plus a CSS file registered in the theme’s asset list. Telling the tool where that list lives prevents a common miss: styles written but never loaded.
Workflow 3: How do you run a site audit with an AI tool?
AI coding tools can write small scripts that check many pages at once. This is useful for audits that would be dull by hand.
- Ask for a script that fetches a list of URLs and checks title length, meta description, H1 count and image alt text
- Run it on a staging copy first
- Review the output as a spreadsheet
- Ask the tool to group issues by template, since one template fix often covers hundreds of pages
- Fix the template, rerun the script and compare
Workflow 4: How do you write content migration scripts?
Moving content from an old site, cleaning HTML or bulk-updating posts is risky. A script written by an AI tool can be fast to produce and dangerous to run.
- Back up the database before any run
- Ask for a dry-run mode that reports changes without saving
- Test on ten records, then a hundred, then all
- Make the script safe to run twice, so a rerun does not create duplicates
- Keep a log of changed record IDs
What rules keep AI coding safe?
These tools act fast and can be wrong with confidence. Rules prevent small errors from reaching visitors.
- Work in version control and review every diff before commit
- Never give the tool production credentials or live API keys
- Run tests and linters after each change, for example php -l for PHP files
- Keep a project notes file updated with conventions and known traps
- Ask the tool to explain any change you do not understand
- Limit permissions: approve commands that delete files or touch the database
A project notes file pays off quickly. When the tool knows that a certain template must not be deleted or that styles live in separate files, it stops repeating the same mistakes.
How do you measure the time saved?
Gut feeling is a poor guide here. Developers often feel faster with AI tools even on tasks where review and rework eat the gain. Track a few numbers for a month instead.
- Time from task start to merged change, by task type
- Number of review rounds before approval
- Bugs found in the week after release
- Reverted commits
Compare the figures with a similar month before the tools. Some task types will show clear gains, others none; shift AI use toward the first group.
How Alien Road applies AI coding tools for websites
In a typical web project, our developers use AI coding tools for theme work, plugin features, audits and migration scripts. Discovery includes reading the existing codebase with the tool’s help and writing the project notes file early.
Setup means a local development environment, version control and a staging site, so no AI change reaches production without review. Each task gets a short written scope before the tool starts.
We measure time per task type and track defects found after release. Reports to clients list delivered features and fixes; the tools are part of how we work, and the review process is what keeps quality steady.
Where to start this week
Pick one small bug from your site’s backlog. Install Cursor or Claude Code on a local copy of the project, describe the bug and ask for the cause first. Review the proposed fix, test it and commit. Time the whole task. If it beats your usual time without new bugs, try a component next week.
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