Key takeaways
- Vibe coding means building by prompting an AI and steering by results, with little line-by-line review.
- A good session starts with a short spec, builds one feature at a time, tests constantly and commits often.
- It carries a negative connotation in professional settings because unreviewed code can hide bugs and security holes.
- AI-assisted coding and 'vibe engineering' keep engineers responsible for design, review and testing.
- Spec-driven development writes requirements first and has AI implement against them, fixing many vibe coding failures.
Vibe coding is building software by describing what you want to an AI coding tool and steering by results rather than reading every line. Done well, it turns ideas into working prototypes in hours; done carelessly, it produces fragile code nobody understands. I use AI coding tools daily on production products, so this guide covers how to vibe code well, a tutorial, roadmap and examples, why the term has a negative edge, how it compares with AI-assisted coding, vibe engineering and spec-driven development, why sessions fall apart, and its future.
What is a good guide to vibe coding?
Start with a clear goal, a small scope and a short written spec, then build in small steps: ask for one feature, run it, test it, and commit to git before the next. Read the parts that matter, especially security and data handling.
Also asked as: guide vibe coding
Principles:
- Choose one tool and learn it well, such as Cursor, Claude Code or a browser builder.
- Keep a rules or instructions file describing your stack and conventions.
- Give precise requests with examples and expected behaviour.
- Paste exact errors and describe what you expected instead.
- Ask the AI to write tests, and run them.
- Commit working states so you can roll back.
What is a simple vibe coding tutorial?
Build a small web app, such as a study planner, in one session: describe it, let the AI scaffold the project, run it locally, then add features one by one with testing after each.
Also asked as: tutorial vibe coding
Tutorial steps:
- Write a five-line spec: users, core feature, data, pages, stack.
- Ask the AI to create the project structure and a minimal working page.
- Run it and fix any errors by pasting them back.
- Add one feature, such as adding and listing tasks with a database.
- Ask for tests for that feature and run them.
- Commit, then add the next feature.
- Deploy to a free host once the core works.
What is a roadmap for learning vibe coding?
Learn basic programming concepts alongside AI tools, so you can judge output: start with simple apps, then add databases, authentication and deployment, then learn testing, security and code review. The goal is moving from vibe coding to confident AI-assisted development.
Also asked as: roadmap vibe coding
A roadmap:
- Weeks 1 to 2: build static sites and small scripts with AI; learn Git.
- Weeks 3 to 4: a CRUD app with a database; read the generated code.
- Month 2: authentication, deployment and environment variables.
- Month 3: tests, debugging without AI, and security basics.
- Ongoing: hackathons and real users, which expose what prototypes miss.
What are some vibe coding examples?
Common examples include personal tools such as habit trackers and expense splitters, event or portfolio websites, internal dashboards that read spreadsheets, browser games, small Chrome extensions, Discord bots, and quick prototypes to test startup ideas.
Also asked as: examples vibe coding
People also vibe code automations, such as scripts that rename files, scrape public data or generate reports, and teaching tools, such as quiz apps for a class. These work well because they are small, low-risk and easy to test by using them. Vibe coding works less well for systems handling payments, health data, many users or complex business rules, which need engineering discipline.
Does vibe coding have a negative connotation?
In professional software circles, often yes. The original meaning, accepting AI code without really reading it, suggests carelessness, and critics associate it with insecure apps, unmaintainable code and inflated claims about replacing engineers.
In other contexts it is neutral or positive, describing how non-programmers and creators can now build useful tools. The connotation depends on what is being built: vibe coding a personal prototype is reasonable; vibe coding a banking backend is not. Many developers now describe their careful AI-assisted work with different terms to avoid the implication that they skip review.
What is the difference between vibe coding and AI-assisted coding?
Vibe coding hands most control to the AI and judges results by whether the app seems to work. AI-assisted coding keeps the developer in control: the AI suggests, drafts and explains, while the developer designs, reviews, tests and takes responsibility for every change.
Also asked as: difference between vibe coding and ai assisted coding
The tools can be identical, such as Cursor, Copilot or Claude Code; the difference is the workflow. AI-assisted developers read diffs, write or require tests, enforce architecture and security, and understand the code they ship. For production software, AI-assisted coding is the professional norm; vibe coding fits prototypes and personal tools.
What is the difference between vibe coding and vibe engineering?
"Vibe engineering" was proposed by developer Simon Willison in 2025 to describe experienced engineers using AI agents responsibly at full speed, with tests, planning, code review, documentation and version control, in contrast to vibe coding's hands-off approach.
Vibe engineering leans on engineering practices that make AI output trustworthy: comprehensive test suites, clear specifications, good project documentation, and the judgement to recognise poor solutions. It treats AI agents like capable but fallible collaborators. The term highlights that AI amplifies existing skills: strong engineers get dramatically more productive, while fundamentals still matter.
What is the difference between vibe coding and spec-driven development?
Spec-driven development writes a clear specification first, covering requirements, design decisions and acceptance criteria, then has AI agents implement tasks against that spec, checking work against it. Vibe coding improvises through prompts without a durable specification.
Also asked as: difference between vibe coding and spec driven development
Tools such as GitHub's Spec Kit and spec-focused AI IDEs formalise this: generate a spec, review it, break it into tasks, then implement and verify. Specs give agents stable context, reduce drift over long sessions and make reviews easier. For anything beyond a small prototype, a lightweight spec pays for itself quickly, which is why many teams combine AI speed with spec discipline.
Why does a vibe coding session fall apart after a couple of hours?
As sessions grow, the conversation context fills with old attempts and errors, the codebase accumulates inconsistent patches, there are no tests to catch regressions, and the AI loses track of earlier decisions, so fixes start breaking other features.
Also asked as: why does your vibe coding session fall apart after hour two
Prevent it by keeping a written spec and rules file the AI always reads, committing working states often, writing tests as you go, starting fresh sessions with a short summary instead of one endless chat, and refactoring when code gets messy. When stuck in loops, stop, read the relevant code yourself, and give the AI precise guidance or revert to the last working commit.
Will vibe coding end like the maker movement?
Some compare vibe coding's hype to the maker movement, which promised everyone would manufacture products with 3D printers but settled into a valuable niche. Vibe coding may follow a similar path as a term, while AI-assisted software creation keeps growing.
The difference is that software is cheap to distribute and AI tools keep improving, so many people will continue building personal tools and prototypes. Professional development will absorb AI deeply but with engineering discipline. Likely outcome: the label fades, the capability becomes normal, and the skills that matter shift towards specifying, verifying and owning quality.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
