Vibe Engineering: The Professional's Guide to AI-Assisted Development
Simon Willison introduces "vibe engineering" as the sophisticated, accountable approach to using LLMs and coding agents while maintaining production-quality standards. Discover how seasoned professionals accelerate their work with AI while staying proudly responsible for their software.
🎯 What You'll Learn
This comprehensive analysis covers Simon Willison's groundbreaking concept of "vibe engineering":
- • The distinction between "vibe coding" and "vibe engineering"
- • Why AI tools amplify existing expertise rather than replace it
- • The 12 key practices that make AI-assisted development effective
- • How coding agents are revolutionizing professional development workflows
- • Real-world insights from the Hacker News community discussion
The Terminology Gap in AI-Assisted Development
Simon Willison identifies a critical gap in how we talk about AI-assisted development. On one end of the spectrum, we have "vibe coding" - the fast, loose, and often irresponsible way of building software with AI that's entirely prompt-driven with no attention to how the code actually works.
But what do we call the other end? Where seasoned professionals accelerate their work with LLMs while staying proudly and confidently accountable for the software they produce? Willison proposes we call this "vibe engineering" - with his tongue only partially in his cheek.
🧠 The Core Distinction
Vibe Coding
- • Fast and loose approach
- • Prompt-driven development
- • No attention to code quality
- • Accept results if they "appear to work"
- • Low-stakes, experimental
Vibe Engineering
- • Sophisticated, accountable approach
- • Production-quality standards
- • Full responsibility for outcomes
- • Iterative improvement with agents
- • Professional-grade results
The Rise of Coding Agents
The landscape of AI-assisted development has been dramatically transformed by the emergence ofcoding agents - tools like Claude Code (February 2025), OpenAI's Codex CLI (April), and Gemini CLI (June) that can iterate on code, actively testing and modifying it until it achieves a specified goal.
Willison notes that experienced, credible software engineers are increasingly running multiple copies of agents simultaneously, tackling several problems in parallel and expanding the scope of what they can take on. This represents a fundamental shift from the traditional "prompt and accept" approach of vibe coding.
🤖 The Agent Revolution
Modern coding agents represent a paradigm shift in AI-assisted development:
- Iterative Development: Agents can test, modify, and improve code in loops
- Parallel Processing: Multiple agents working on different problems simultaneously
- Goal-Oriented: Agents work toward specific objectives rather than just generating code
- Quality Assurance: Built-in testing and validation capabilities
- Context Awareness: Understanding of project structure and requirements
Why AI Tools Amplify Existing Expertise
One of Willison's most profound insights is that AI tools amplify existing expertise. The more skills and experience you have as a software engineer, the faster and better the results you can get from working with LLMs and coding agents.
This isn't about AI replacing human expertise - it's about AI multiplying the impact of human expertise. The tools reward and enhance the practices that experienced engineers already know are important.
The 12 Key Practices of Vibe Engineering
Willison identifies twelve critical practices that make AI-assisted development effective. These aren't new practices - they're the same ones that top-tier software engineers have always used. The difference is that AI tools make these practices even more valuable.
🧪 1. Automated Testing
If your project has a robust, comprehensive test suite, agentic coding tools can fly with it. Without tests? Your agent might claim something works without having actually tested it.
Key insight: Test-first development is particularly effective with agents that can iterate in loops.
📋 2. Planning in Advance
Sitting down to hack something together goes much better if you start with a high-level plan. Working with an agent makes this even more important.
Key insight: You can iterate on the plan first, then hand it off to the agent to write the code.
📚 3. Comprehensive Documentation
Just like human programmers, an LLM can only keep a subset of the codebase in its context at once. Good documentation lets it use APIs from other areas without reading the code first.
Key insight: Write good documentation first and the model may build the matching implementation from that alone.
🔧 4. Good Version Control Habits
Being able to undo mistakes and understand when and how something was changed is even more important when a coding agent might have made the changes.
Key insight: LLMs are fiercely competent at Git - they can navigate history and use git bisect better than most developers.
⚙️ 5. Effective Automation
Continuous integration, automated formatting and linting, continuous deployment to preview environments - all things that agentic coding tools can benefit from too.
Key insight: LLMs make writing quick automation scripts easier, which helps them repeat tasks accurately.
👥 6. Culture of Code Review
If you're fast and productive at code review, you're going to have a much better time working with LLMs than if you'd rather write code yourself.
Key insight: This one explains itself - review skills become even more critical with AI-generated code.
🎯 7. Management Skills
Getting good results out of a coding agent feels uncomfortably close to getting good results out of a human collaborator.
Key insight: You need clear instructions, context, and actionable feedback - but it's easier than managing people.
🔍 8. Manual QA Expertise
Beyond automated tests, you need to be really good at manually testing software, including predicting and digging into edge cases.
Key insight: Human intuition for edge cases becomes even more valuable when working with AI.
🔬 9. Strong Research Skills
There are dozens of ways to solve any given coding problem. Figuring out the best options and proving an approach has always been important.
Key insight: Research skills remain a blocker on unleashing an agent to write the actual code.
🚀 10. Preview Environment Shipping
If an agent builds a feature, having a way to safely preview that feature without deploying it straight to production makes reviews much more productive.
Key insight: Preview environments greatly reduce the risk of shipping something broken.
🎯 11. Intuition for Outsourcing
An instinct for what can be outsourced to AI and what you need to manually handle yourself. This is constantly evolving as the models and tools become more effective.
Key insight: Maintaining a strong intuition for when AI can best be applied is crucial.
⏱️ 12. Updated Estimation Skills
Estimating how long a project will take has always been one of the hardest parts of being a senior engineer. AI-assisted coding makes this even harder.
Key insight: Things that used to take a long time are much faster, but estimations now depend on new factors.
The Reality of Professional AI-Assisted Development
Willison makes a crucial point: if you're going to really exploit the capabilities of these new tools, you need to be operating at the top of your game. You're not just responsible for writing the code - you're:
🎯 The Expanded Role of the AI-Assisted Engineer
- • Researching approaches and deciding on high-level architecture
- • Writing specifications and defining success criteria
- • Designing agentic loops for complex workflows
- • Planning QA and quality assurance strategies
- • Managing a growing army of weird digital interns who will absolutely cheat if you give them a chance
- • Spending so much time on code review - more than ever before
Almost all of these are characteristics of senior software engineers already! The difference is that AI tools amplify these existing skills and make them even more critical for success.
Why "Vibe Engineering" as a Term?
Willison acknowledges that "vibe engineering" might be a stupid name. "Vibes" as a concept in AI feels a little tired at this point, and "vibe coding" itself is used by a lot of developers in a dismissive way. But he's ready to reclaim vibes for something more constructive.
🎭 The Strategic Choice of Terminology
Willison chose "vibe engineering" for several strategic reasons:
- • Clear Distinction: Establishes a clear distinction from vibe coding
- • Signals Sophistication: Indicates this is a different, harder, and more sophisticated approach
- • Controversial by Design: Intentionally cheeky and likely to be controversial
- • Self-Contradictory: The mismatch between "vibes" and "engineering" makes it memorable
- • Reclaiming the Term: Taking back "vibes" from dismissive usage
He notes that he's tried in the past to get terms like "AI-assisted programming" to stick, with approximately zero success. "May as well try rubbing some vibes on it and see what happens."
Community Response: Hacker News Insights
The Hacker News discussion on Willison's post reveals a mixed but generally positive sentimentfrom the developer community. Here's what the community is saying:
✅ Positive Reactions
- • Many developers appreciate the distinction between irresponsible AI use and professional AI-assisted development
- • Several commenters relate to the challenges of working with coding agents effectively
- • The concept resonates with experienced developers who've struggled with AI tool adoption
- • Recognition that this represents a fundamentally new approach to development
🤔 Critical Views
- • Some question whether "vibe engineering" is just rebranding of existing best practices
- • Concerns about the term "vibe" being overused in AI discussions
- • Skepticism about whether this represents a fundamentally new approach
- • Questions about the practical applicability for different types of projects
💡 Practical Insights from the Community
- • Real-world experiences: Developers sharing their own experiences with coding agents and parallel AI workflows
- • Learning curve discussions: Comments on the steep learning curve for effective AI-assisted development
- • Quality standards: Emphasis on the importance of maintaining code quality standards with AI tools
- • Tool evolution: Discussion of how AI tools are rapidly evolving and changing development practices
The Future of Professional AI-Assisted Development
Willison's concept of "vibe engineering" represents more than just a clever term - it's a framework for understanding how professional software development is evolving in the age of AI. The key insight is that AI tools don't replace human expertise; they amplify it.
As coding agents become more sophisticated and capable, the role of the human engineer becomes increasingly focused on high-level decision-making, architecture, and quality assurance. The engineers who thrive in this new environment will be those who can effectively manage and direct AI tools while maintaining the highest standards of software quality.
🔮 Key Predictions for the Future
- • Specialization will increase: Engineers will need to become experts in AI tool management
- • Quality standards will rise: AI tools will enable higher quality standards, not lower them
- • Architecture becomes critical: High-level design and planning skills become even more valuable
- • Review skills are essential: Code review and quality assurance become primary responsibilities
- • Continuous learning: The pace of AI tool evolution requires constant adaptation
Conclusion: Embracing the Vibe Engineering Mindset
Simon Willison's "vibe engineering" concept provides a valuable framework for understanding how to approach AI-assisted development professionally. Rather than seeing AI tools as a replacement for human expertise, it positions them as amplifiers of existing skills and practices.
The twelve key practices he identifies aren't new - they're the same practices that top-tier software engineers have always used. The difference is that AI tools make these practices even more valuable and necessary for success.
As we move forward in this new era of AI-assisted development, the engineers who will thrive are those who can effectively combine human expertise with AI capabilities, maintaining the highest standards of software quality while leveraging the power of modern AI tools.
Whether you embrace the term "vibe engineering" or not, the underlying principles represent a roadmap for professional AI-assisted development that maintains accountability, quality, and human expertise at its core.
📝 Source Attribution
This analysis is based on Simon Willison's original blog post "Vibe engineering" published on October 7, 2025, and the subsequent Hacker News discussion.
Primary sources:
This analysis distills Willison's insights into actionable principles for professional AI-assisted development, incorporating community feedback and practical considerations.