Exploring Vibe Coding - Early Impressions and Practical Framework

I recently spent some time experimenting with Vibe Coding, and I have to say, its efficiency and output quality have exceeded my expectations.

A few months ago, my experience with AI-assisted coding was still quite primitive, especially around large-scale code handling and the use of native developer tools. Now, the improvements are significant.

It’s worth noting that my observations are based on well-defined, complex production-oriented goals, not just “one-line prompt → code output” type experiments. What follows reflects hands-on, production-level exploration.

The Key Takeaways

1. Know Exactly What You Want

In the world of Vibe Coding, clarity of intent is everything.

This includes not just functional goals and process flows, but also your taste and aesthetic preferences.
Think of it this way: “How” is no longer your main concern, focus instead on “What”. The AI will take care of much of the rest.

2. Process, Standards, and Tooling Guidelines

Vibe Coding empowers designers, product managers, and other non-technical roles to participate directly in development.

However, powerful tools can also introduce chaos if misused.
Establishing clear processes, conventions, and tool usage guidelines ensures smooth collaboration. It keeps everyone aligned under the same framework, prevents disorder, and makes the overall workflow more efficient and controllable.

3. Give Clear Descriptions and Expectations

If you’re building serious products or business logic, Vibe Coding is not a one-liner magic wand.

Treat it like real product or engineering design: always provide clear descriptions and expectations.
Avoid oversimplifying into “one sentence → wait for results,” as that often loses crucial logic and context.

4. Complexity Still Requires Professionalism, Rigor, and Completeness

Complex product and business logic are inevitable, and they’re exactly where AI coding struggles the most.

Your best weapon is rigorous, complete, and well-structured input, clear logic, well-defined goals, and explicit constraints.
Doing so helps the model avoid fragmented or arbitrary output, and ensures it understands the full picture.

From experience, when you provide structured, professional context, the AI often can deliver surprisingly solid code.

5. Follow Software Engineering Fundamentals, Adapt Them to AI Development

Foundational engineering principles still hold true:
conventions, checks, testing, logical validation, verification, they remain your safety net.

But AI-assisted development changes the dynamics:
AI runs 24/7, consumes credits, and can iterate endlessly.
Thus, you must adjust traditional methodologies to fit this new rhythm, optimize for iteration speed, prompt clarity, and verification rather than manual debugging.

6. Two-Way Interaction and Multi-Perspective Communication

Effective Vibe Coding is not a solo performance.

Treat the AI as a mid-level engineer, intelligent but limited in understanding.
Maintain two-way communication across roles (product, design, engineering, QA).

You can formalize these perspectives into functional agents, but even with automation, human-in-the-loop dialogue remains essential.

7. Identify and Leverage the Right Tools

AI coding tools are evolving rapidly, with specialized options emerging across languages and roles.

Choosing the right set of tools can significantly improve efficiency, especially for non-technical contributors.
That said, the ecosystem is uneven in quality.
Prefer tools that have been tested and reviewed, rather than chasing every shiny new product.

8. Most Effective for Those with Strong Ideas, Aesthetics, and Engineering Maturity

Vibe Coding enables instant translation of ideas and aesthetics into output, but it’s still far from replacing serious engineering work.

Developers with solid engineering foundations can minimize uncertainty while aligning AI output with production standards.

Such hybrid creators, who combine conceptual clarity, aesthetic sense, and technical discipline, can achieve results far beyond traditional productivity.
Meanwhile, roles that rely purely on “know-how” or “speed” may find their advantage shrinking as AI closes that gap.

Practical Notes: How to Structure AI-Assisted Development

1. The Development Process

1.1 Input & Communication

• Communicate requirements clearly, with text, diagrams, or structured documents.
• Tackle one task at a time (“one PR, one problem”).
• For large tasks, define the overall framework first and iterate with AI in smaller steps.
• If your work relates to existing implementations, have AI review and align before proceeding.
• Use separate threads for complex topics to prevent context corruption.
• Always clarify both technical (flows, algorithms, edge cases) and experience (UI, visuals, UX goals) details.
• For UI/UX, professional-level detail is required to avoid “AI-generated flavor.”
• When defining APIs, include login requirements, permission rules, caching/DB considerations, and performance constraints.
• Use positive guidance (“what to do”) rather than vague negatives (“don’t do X”).
• Treat AI like a junior teammate, reinforce what you want repeatedly.
• Use feature flags to isolate experimental outputs and enable safe rollback.
• Consolidate common constraints in a shared rules or AGENTS.md file.

1.2 Specification Confirmation

• Feed the full requirements to AI and ask it to restate them for understanding checks.
• Co-develop specs, design docs, or task plans before implementation.
• If instructions keep failing, change the framing or angle of description.

1.3 Output Verification

• Maintain good engineering hygiene: formatting, linting, and tests.
• Prioritize smoke, integration, and end-to-end tests.
• Reduce unit test density early when code churn is high.
• Interrogate the implementation in natural language: Did it only change what was necessary? Does it behave as expected? Are user flows smooth?

1.4 Submitting Pull Requests

• Let AI help with PR creation, but consider doing it manually to save credits and keep control.
• Keep branch strategies simple (feature/fix).
• Use CI/CD pipelines (lint, test, preview deploys) for every PR.
• Review Vercel previews or sandbox environments for manual validation.

1.5 Testing and Iteration

• Pass error logs or compiler messages directly to AI; escalate to humans only after repeated failures.
• Provide explicit bug diffs and examples; ask AI to generate integration tests.
• Always review UX and API flows manually.
• Avoid full-log AI debugging, inefficient and costly.
• If a thread degrades (“context rot”), restart a new one instead of fighting it.

1.6 Fast Deployment

• Favor trunk-based development: merge and deploy once CI passes.
• Always run integration and E2E tests before merging.
• Use feature flags for uncertain or large changes.
• Roll back quickly if issues appear.
• Once user scale grows, add staging or canary environments.

1.7 Cross-Cutting Work

• Let AI handle reusable tasks (style abstractions, utility frameworks).
• Schedule regular refactors, code reviews, and test suite improvements.
• Combine AI’s code analysis with manual reviews to build integration test libraries and align documentation.
• Define self-check routines (e.g., consistent button styles, code smell detection).
• Keep the AGENTS.md file updated, define “what to do” and “what to avoid.”
• Perform periodic housekeeping and cleanup.
• Once the foundation is solid, future “vibe follow” work becomes much easier.

2. Team Collaboration

• Non-engineers can now close loops directly, reducing friction and boosting velocity.
• Engineers focus on reviewing critical logic and solving complex issues.

3. Observed Outcomes

• AI now handles most of the business-layer implementation.
• Both deployment and validation processes have become significantly faster.

4. Additional Notes

• For deterministic tasks, always be explicit; for creative ones, define boundaries and expectations.
• AI’s reasoning remains mechanical, completeness of input is key.
• Treat AI as a tireless mid-level developer, it needs context and clear communication.
• Post-PR testing and automation must continue to evolve toward production-grade standards.
• Classic engineering practices will need re-evaluation under AI-augmented workflows.
• We’re moving from AI Coding to AI-driven Marketing, Product, and Ops, forming early versions of “full-stack AI teams.”

Final Thoughts

With AI’s expanding breadth and speed, traditional software engineering methods are being reshaped in real time.

There’s no single “stable” process yet, teams must constantly adapt to new tools and shifting paradigms.

From my experience, Vibe Coding still requires domain expertise, strong process discipline, and collaborative rigor to reach production reliability.
Engineers and “AI maintenance” roles must work closely with models to achieve consistent results.

But given the current trajectory, one of the most exciting frontiers ahead may well be the rise of autonomous AI engineering teams, systems capable of designing, building, and maintaining software at scale.