How Vibe Coders Build Apps with Accessibility at the Core

This work began with a small frustration: a user who could not navigate a checkout flow. That moment changed a team’s idea of speed and quality. They learned that fast, AI-driven development must still respect real people who rely on clear structure and labels.

Today, the term coined by Andrej Karpathy in early 2025 describes development guided by natural language prompts. When AI generates code, it can move at unheard-of speed—but it can also produce missing headings, overused spans, and unfocusable navigation.

Teams can embed checks at the start of their workflow by prompting for semantics, reviewing roles, and running in-IDE scans with modern tools. Tools like Deque’s axe MCP Server integrate into that loop—scan, remediate, and re-scan—so teams keep quality high while shipping fast.

Key Takeaways

• AI-generated code is fast but needs semantic reviews to serve all users.
• Adopt a prompt-to-validate workflow: set intent, generate, test, and fix.
• Use MCP-integrated tools—see practical guidance from a Deque case—to align speed with quality.
• Simple semantic fixes—headings, labeled controls, proper roles—improve experience immediately.
• Balance innovation with responsibility: automate where possible and include human checks.

Why accessibility must anchor vibe coding in the present

AI-assisted workflows accelerate interface creation, but speed alone cannot guarantee usable products.

Generated interfaces often omit headings, landmarks, and accessible names. That gap turns a working application into a puzzle for keyboard and screen reader users.

Accessibility is not a “nice to have”—it is a present requirement because rapid output multiplies defects. When teams rely on natural language prompts, they can skip semantics and introduce navigation gaps or unlabeled controls.

Anchoring accessibility early cuts total time to quality. It prevents rework, reduces rushed hotfixes, and limits the compounding of content or UX errors across a project.

• Translate purpose into testable steps and run checks per step.
• Define semantics, focus order, and consistent language before writing code.
• Review AI outputs like any other code: scan, fix, and verify.

Responsible teams protect users and the product: they lower legal risk, preserve trust, and ensure the experience works for everyone—now and over time.

Understanding vibe coding: what it is, how it works, and where it breaks

Modern workflows let developers describe intent in plain language and receive runnable code back. This shift changes how teams plan, test, and own software versions.

Definition and origins

Vibe coding began as a conversational development pattern in 2025 when practitioners started using natural language prompts to guide models that generate UI and logic. Andrej Karpathy popularized the term and framed it as an approach where writing code is close to writing intent.

Two modes of practice

Pure prototyping favors speed: teams explore ideas fast, accept rough syntax, and iterate visually.

Responsible AI‑assisted development pairs generation with human review, tests, and explicit standards so each version is safe to ship.

• Tight loop: generate → run → refine for quick experiments.
• Full lifecycle: document, test, and audit before production release.

Common pitfalls and how they surface

Models can produce sloppy structure: excess spans, fragmented headings, and missing landmarks like <header> or <main>. Unlabeled controls and redundant components make navigation brittle.

“Speed without rules compounds defects quickly—small errors in generated UI scale across releases.”

Teams reduce risk by encoding naming, roles, and test steps into prompts. For practical tool guidance, consult a comprehensive guide.

Foundations first: principles to make accessibility part of the vibe

Embedding structure in prompts turns rapid output into reliable, testable pages. Start by mapping intent: decide headings, landmarks, and focus order before generation. That upfront work prevents inconsistent labels and content debt.

Shift-left mindset: plan headings, landmarks, navigation, and focus order

Shift-left accessibility asks the team to design semantics early so assistive tech parses the page reliably. Plan heading levels, main and complementary landmarks, and logical tab order for keyboard users.

Map each screen’s intent and the exact text a user needs at each step. Use prompts that encode roles, expected labels, and error patterns to improve output quality and reduce rework.

Content design guardrails: define terminology early to prevent content debt

Establish a simple glossary so language stays consistent across the product. Mismatched terms force users to guess and increase abandonment risk.

• Define evaluation criteria: clarity, inclusivity, and actionability.
• Treat microcopy as part of interaction design: labels, statuses, and instructions matter.
• Document decisions and the workflow so the team can scale patterns with confidence.

From idea to app: an accessible workflow across tools and teams

Teams can assemble a practical pipeline that turns an idea into a working, inclusive application. Start with clear goals, then use each tool to validate semantics and flow before deployment.

Google AI Studio: prompting with goals and structure

Begin in Google AI Studio by stating accessibility goals alongside features. For example: use semantic HTML, ARIA only when necessary, and include labeled buttons.

Firebase Studio: blueprint review for roles and flows

Use Firebase Studio’s blueprint to confirm landmarks, heading hierarchy, focus order, and error states. Validate the plan before generating a prototype so the team avoids rework.

Gemini Code Assist: in‑IDE generation, refactors, and tests

Move generated code into Gemini Code Assist to refine components and add unit tests. Keep a11y checks close to development so developers can verify labels and keyboard interaction fast.

UX essentials: headings, labels, buttons, and error messages

Capture UX basics: meaningful headings, descriptive labels, clear buttons, and error messages that guide recovery. Annotate flows with roles and expected interactions, then confirm the working prototype matches the plan.

• Treat each step as a chance to validate assumptions.
• Adjust prompts and code until behavior is predictable for every user.
• Deploy only after review confirms logic and interaction are coherent and maintainable.

vibe coding accessibility: test, fix, and verify at the speed of AI

Short, repeatable test‑remediate loops keep pages working even as AI generates new code. Teams should run scans where they edit so findings translate into immediate fixes. That reduces context switching and speeds up verification.

Plug in MCP: connect axe MCP Server to your IDE and agents

Connect axe MCP Server to editors and agents (Copilot, Windsurf, Cursor) to run a full scan in-context. The server returns precise, code-level fixes so developers can accept or adapt recommendations without leaving the IDE.

Effective prompts: analyze, remediate, and reanalyze with natural language

Use concise natural language prompts to keep the loop tight. For example:

#analyze http://localhost:3000; #remediate …; reanalyze

Typical issues to catch: spans-as-text, missing landmarks, and name/role/value

• Expect exact findings: missing landmarks, fragmented headings, spans used as interactive text, and missing name/role/value metadata.
• Prioritize fixes that restore semantics and keyboard operability before visual polish.
• Re-run testing after each change and pair automated results with a quick manual check for custom widgets.

Closing the loop: automated scans, human review, and regression protection

Close the loop by treating scans, human checks, and tests as a single, repeatable habit.

Ship responsibly: move from ad hoc checks to a continuous workflow that runs automated scans in CI for every pull request. Block regressions early and reduce surprises in production.

Pair MCP-enabled axe scans in the IDE with unit testing to protect semantics. Extend tests to assert accessible names, focus order, and keyboard behavior so testing covers real interactions.

Practical steps for teams

• Run CI scans on each merge request to catch regressions before merge.
• Write unit tests that verify names, roles, and focus—treat them as functional coverage.
• Schedule manual screen reader and keyboard reviews; human checks find subtle problems automation misses.
• Have developers triage by severity and user impact so the highest-risk error gets fixed first.
• Maintain a shared dashboard to track violations, remediation status, and trends—make quality visible to the whole team.

Document every step of the release checklist: scan, fix, re-scan, and manual verification. For complex applications, add targeted scripts that assert role, state, and name to prevent subtle regressions. Treat testing as an ongoing product investment; consistent effort keeps the user experience reliable as features evolve.

Real-world example: turning a span-heavy UI into an accessible, navigable page

A common real-world fault is an AI-generated header where semantic tags are replaced by many spans and interactive text is not keyboard-focusable.

The failing React snippet showed many spans in a header, nav text that could not receive focus, and a cart button with no accessible name. Screen reader output was fragmented; users could not reliably find the main content.

Replace spans with semantic HTML, proper headings, and labeled buttons

Step one: swap <span> chains for true landmarks—use <header>, <nav>, and a single <h1> for the hero.

Convert nav items to links and make the cart a keyboard-focusable <button> with a clear accessible name such as “Basket, 3 items”.

Add landmarks and focusable navigation; verify fixes via axe and screen readers

• Run axe MCP Server in the IDE to analyze the failing page and accept code-level fixes.
• Re-scan; confirm the issue count for this flow drops to zero.
• Manually tab through the app and use a screen reader to check name/role/value announcements and visible focus.

“Replace spans with semantic elements and add accessible names to all interactive controls.”

Practice: commit small, clear changes and document why the code changed. Capture prompts that worked in your workflows and reuse them across apps to build a reliable pattern library.

Conclusion

Responsible teams pair rapid AI output with clear review gates so each app ships with intent and stability.

Use AI to accelerate ideas, not to skip validation. Keep a lightweight blueprint of semantics, labels, and flows so every application feature inherits proven patterns.

Normalize testing in CI and run in‑IDE scans with the right tool—then fix and reanalyze before merge. Maintain a reusable library of prompts, patterns, and examples to cut time on future projects.

For practical guidance on risks and hybrid approaches, see a concise review on the rise and risks of this method: the rise and risk of vibe.

Prototype quickly, review carefully, fix decisively, and measure results with the proper tools. That way developers turn rapid generation into reliable software that users can trust.