What is vibe coding? How can it make you faster?

If you’ve ever described what you want to an LLM and watched it generate working code in seconds, you’ve done vibe coding.

The term might be new, but the practice is already everywhere. 84% of developers now use AI coding tools. Some startups ship 95% of their code via AI. And Claude Code wrote roughly 90% of Claude Code itself.

AI-assisted development works differently than traditional coding. Teams using it for production code are learning that velocity and quality need different approaches than they did with prototypes. The developers seeing the biggest wins treat AI as a powerful collaborator rather than a replacement.

What is vibe coding?

Vibe coding is a chatbot-based approach where you describe what you want to a large language model, and it generates the code. You’re working from the “vibe” of what needs to happen rather than meticulously planning and writing every line yourself.

Andrej Karpathy coined the term in February 2025. By December, Collins Dictionary had crowned it Word of the Year.

Karpathy originally positioned it for prototyping and “throwaway weekend projects”, contexts where understanding every implementation detail matters less than shipping fast.

But teams started using it for production code. The approach that worked for hackathons got applied to customer-facing systems, API integrations, and business-critical applications.

Key AI coding assistant statistics for 2026

Adoption is massive:

• 80-85% of developers using or planning to use AI tools
• 78% report productivity improvements
• Developers save ~3.6 hours per week on average
• 90% of Fortune 100 companies use AI coding tools

But quality hasn’t kept pace:

• AI-coauthored code has 1.7x more major issues
• Security vulnerabilities 2.74x higher in AI-generated code
• 45% of developers report debugging AI-generated code takes longer than writing code manually
• 66% struggle with AI outputs that are “almost correct” but flawed

Vibe coding vs. traditional development

Traditional programming means writing code line by line. You’re thinking in syntax, logic, and structure as you build. Vibe coding works differently. You describe what you want in plain language, and AI writes the implementation. Neither replaces the other. Most professional workflows blend both.

Say you need a user login form. Traditional approach: you write the HTML structure, add input validation, handle form submission, and manage error states. Vibe coding approach: you prompt “create a user login form with email validation and error handling” and review what gets generated. Karpathy also calls it “software 3.0”, and explains that “the hottest new programming language is English”.

Here’s how they compare:

AI as your fastest junior developer

Think of your AI assistant as the fastest junior developer in the world. Incredibly productive. Surprisingly capable. But still needs constant direction, context, and review.

You wouldn’t let a junior engineer ship code without review. The same rules apply here.

Role definition that works

Human as navigator:

• Architecture decisions
• Security requirements
• Code review and quality gates
• Strategic direction

AI as driver:

• Code generation
• Boilerplate automation
• Test case expansion
• Refactoring suggestions

This separation prevents both over-reliance (shipping unreviewed AI code) and under-utilization (manually writing boilerplate that AI could generate in seconds).

The context-communication-review loop

Vibe coding follows a three-step pattern:

1. Set context

AI doesn’t know your codebase, your standards, or your constraints – you have to tell it.

Good context includes:

• Project goals and constraints
• Coding standards and style guides
• Architecture patterns you’re using
• Integration requirements

2. Prompt with precision

Vague prompts get vague results. Specific prompts get code you can actually use.

Don’t: “Add error handling”

Do: “Add try-catch for network timeouts on the HTTP client. Log failures to structured JSON with request ID, timestamp, and error message. Retry 3 times with exponential backoff starting at 100ms.”

3. Review critically

This is non-negotiable. Every piece of AI-generated code needs human review for:

• Logic correctness
• Security vulnerabilities
• Performance implications
• Maintainability

Vibe coding anti-patterns (and how to avoid them)

Anti-pattern 1: Generate and ship

Problem: Accepting AI code without review, especially in production.

Solution: Mandatory code review gates. Automated testing before merge. Treat AI output like any other external contribution.

Anti-pattern 2: Black box dependency

Problem: Your team can’t debug or maintain code they don’t understand.

Solution: Have an understanding or refactor rule. If you can’t explain what the code does and why, don’t deploy it.

Anti-pattern 3: Context-free prompting

Problem: Generic prompts yield generic (and often wrong) results.

Solution: Maintain rich context documents. Use architecture decision records. Provide examples of what good looks like.

Anti-pattern 4: Security through obscurity

Problem: Assuming AI knows your security requirements.

Solution: Explicit security checklists. OWASP Top 10 validation. Secrets management enforcement. Never trust AI to “just know” security best practices.

Best practices for professional workflows when vibe coding

Start with tests, not implementation

Test-driven development works exceptionally well with AI. Write your test cases first, then have AI implement code to pass them.

Workflow: Adding email validation to the sign-up API

1. You write test cases:

• Valid email formats should succeed
• Invalid formats return 400 with a specific error
• Duplicate emails return 409

2. AI generates validation logic to satisfy tests

3. You review for edge cases, security, and performance

Build to-do lists before building features

Have AI create structured task breakdowns before writing code. This prevents “chaos coding,” where you describe the end product, but the path to get there is unclear.

Example prompt: “Create a to-do list for implementing OAuth 2.0 authentication that integrates with our existing user service.”

The breakdown keeps both you and your AI assistant aligned on architectural coherence.

Implement quality gates

Professional development requires enforcement mechanisms:

• Automated testing (unit, integration, E2E)
• Static analysis and linting
• Security scanning (SAST/DAST)
• Code coverage thresholds
• Peer review requirements
• Performance benchmarking

Use AI for boilerplate, human for architecture

AI-friendly tasks:

• CRUD operations
• Data transformations
• Test data generation
• API client boilerplate
• Configuration file templates

Human-driven decisions:

• System architecture
• Database schema design
• Authentication flows
• Data privacy decisions
• API contract definitions

Choosing your tools

Vibe coding apps like Cursor, Windsurf, and Replit, alongside LLM models like Claude Opus 4.6 (for long context) and Gemini 3 Pro (for reasoning), have transformed how developers prototype and build.

From all the options available, it would be best to choose based on your workflow:

• Building API integrations with detailed specifications? Context window size matters.
• Prototyping quickly? Speed matters.
• Working in regulated industries? Data residency and compliance matter.
• Large existing codebase? IDE integration matters.

Test a few. Stick with what actually makes you faster without sacrificing quality.

Looking ahead: Beyond vibe coding

The industry is already evolving from vibe coding to “agentic coding”, autonomous AI agents that don’t just generate code on request but proactively suggest improvements, write tests, and even review other AI’s output.

AI-on-AI code review is emerging. Continuous AI assistance is integrating into CI/CD pipelines. AI literacy is becoming a core developer skill.

The question isn’t whether AI will transform software development, it already has. The question is whether engineering teams will develop the discipline to harness AI’s velocity without sacrificing the rigor that production systems demand.

Vibe + vigilance

Vibe coding is a tool, not a methodology. Speed matters, but so does responsibility.

Software engineering has always balanced speed with quality, and AI-assisted development doesn’t change that fundamental principle.

• Human oversight is non-negotiable
• Quality gates protect production
• Code review catches what automation misses
• Understanding your code is still your responsibility

Whether you’re building internal tools or customer-facing applications on Infobip’s communications platform, let AI amplify your expertise, not replace your judgment.

What matters is the quality of customer communications you build on top of the infrastructure. AI can generate the code that sends a message, but understanding when to send it, which channel to use, and how to handle failures? That requires the kind of strategic thinking that still belongs to you. The conversations your customers have with your business need to work flawlessly, whether the underlying code was written by AI in seconds or crafted manually over hours.

The best code is still written by humans. It’s just that now, humans have the world’s fastest junior developer ready to help.

Frequently asked questions (FAQs)