Human‑Driven vs AI‑Driven IDEs

AI‑powered IDEs such as Cursor, VS Code with Copilot, and WebStorm AI Assistant are rapidly becoming part of everyday development. They can autocomplete code, refactor components, generate tests, and explain unfamiliar APIs.

There is, however, a crucial distinction in how these tools are used:

• AI‑driven development: the IDE leads, the human reacts.
• Human‑driven development: the human leads, the AI assists.

This article explains that distinction, why human‑driven workflows matter for teams, and how to configure AI‑enabled IDEs (with Cursor as a concrete example) to support that approach.

What Do We Mean by AI‑Driven?

An AI-driven workflow is one in which the developer accepts large AI-generated blocks of code with little or no critical review, allows the AI to make decisions about architecture, naming, and patterns, and increasingly relies on the tool to “solve” problems instead of reasoning through them. In this model, the AI effectively leads the development process, while the human mainly reacts to its output.

// Prompt: "Create a responsive navbar in React"

export default function Navbar() {
 return (
  <nav>
   <ul>
    <li>Home</li>
    <li>About</li>
    <li>Contact</li>
   </ul>
  </nav>
 );
}

This code works, but the AI has implicitly made design decisions on behalf of the team, without considering accessibility, responsiveness, design‑system conventions, or state management. These omissions are subtle, but they accumulate quickly in real codebases.

What Is Human‑Driven Development?

Human‑driven development keeps intent, judgement, and responsibility firmly with the developer.

In a human-driven approach, the AI is treated as a fast typist, a refactoring assistant, and a source of suggestions rather than a decision-maker. The human developer defines what needs to be built and why, while the AI helps with how to implement it, and all generated code is carefully reviewed, understood, and adapted to fit the wider system and context.

interface NavItem {
 label: string;
 href: string;
}

const navItems: NavItem[] = [
 { label: 'Home', href: '/' },
 { label: 'About', href: '/about' },
 { label: 'Contact', href: '/contact' },
];

export function Navbar() {
 return (
  <nav aria-label="Main navigation">
   <ul className="nav-list">
    {navItems.map(item => (
     <li key={item.href}>
      <a href={item.href}>{item.label}</a>
     </li>
    ))}
   </ul>
  </nav>
 );
}

Here, the AI may have helped with typing or mapping logic, but the structure is intentional, accessibility is explicit, and data is separated from presentation

Using Cursor as a Human‑Driven IDE

Using Cursor effectively requires deliberate constraints. Its deep AI integration can easily shift control away from the developer if left unchecked.

A human‑driven setup starts by making intent explicit before invoking AI, through comments, partial implementations, type definitions, or clearly stated constraints. This anchors suggestions in the developer’s reasoning and ensures that the tool responds to existing context rather than introducing unsolicited structure or assumptions.

In practice, Cursor works best when used incrementally. Inline completions and small, reviewable changes encourage understanding and ownership. Conservative configuration helps prevent large, unexamined rewrites. Treated this way, Cursor becomes an accelerator for well‑defined ideas rather than a substitute for architectural thinking or judgement.

Configuration Files for a Human‑Driven Cursor Setup

A key part of keeping Cursor human‑driven is making your preferences explicit in configuration files. This reduces accidental AI overreach and aligns the tool with team standards.

1. Project‑Level Rules (.cursor/rules.md or similar)

Use a project‑local rules file to describe how the AI should behave.

# AI Usage Rules

- Do not introduce new libraries without asking
- Prefer existing project patterns over generic best practices
- Optimise for readability over cleverness
- Always respect accessibility (WCAG) concerns
- Never change public APIs unless explicitly requested

This works especially well in teams, as it makes expectations explicit and repeatable.

2. Editor Settings (settings.json)

Cursor builds on VS Code, so editor configuration still matters.

{
  "editor.inlineSuggest.enabled": true,
  "editor.suggest.preview": true,
  "editor.acceptSuggestionOnEnter": "off",
  "cursor.ai.autoGenerate": false,
  "cursor.ai.inlineSuggestions": true,
  "cursor.ai.confirmLargeChanges": true
}

These settings bias toward small, intentional interactions rather than wholesale generation.

3. Repository Documentation (CONTRIBUTING.md)

Document how AI is expected to be used in the repository.

## AI‑Assisted Development

AI tools may be used for:
- Refactoring
- Test generation
- Documentation improvements

AI tools should not be used for:
- Designing core architecture
- Introducing new patterns without review
- Replacing code reviews

This makes AI usage part of the team’s social contract, not an implicit or ad‑hoc practice

4. Optional: Prompt Templates

Some teams keep prompt snippets in the repo for consistency.

## Refactoring Prompt

Refactor the selected code:
- Keep the public API unchanged
- Do not introduce new dependencies
- Prefer clarity over abstraction
- Explain the changes briefly

This reduces vague prompts and improves output quality.

Human‑Driven Prompting Patterns

Instead of vague prompts like "Build this component", use constrained prompts such as:

• "Refactor this function to be more readable, keep the API unchanged"
• "Suggest accessibility improvements only, no visual changes"
• "Explain this code rather than rewriting it"

One effective pattern is using AI for review, not creation. The AI becomes a second pair of eyes, not the primary author.

// Ask the AI:
// "What edge cases am I missing here?"

function formatPrice(value: number) {
  return `£${value.toFixed(2)}`;
}

Why Human‑Driven Matters in Front‑End

Front‑end code is not just about logic; it directly shapes user experience, accessibility, performance, and visual consistency. Every component encodes product decisions and design intent.

AI models have no real understanding of your users, business goals, regulatory constraints, or design system nuances. When they lead, those concerns are flattened or ignored.

Human‑driven workflows help teams:

• Avoid fragmented UI patterns
• Preserve long‑term code quality
• Reduce hidden technical debt
• Improve readability and onboarding
• Maintain shared understanding across the codebase

By keeping intent and judgement with developers, teams gain speed without sacrificing coherence.

Conclusion

AI‑enabled IDEs are not inherently good or bad; the real risk lies in ceding intent.

By defining goals clearly, configuring tools conservatively, and treating AI as an assistant rather than an architect, developers can benefit from increased velocity without losing understanding or ownership.

Human‑driven development is not slower: it is deliberate. And in UI‑centric codebases, that deliberation is a feature, not a bug.