Practical vibe coding techniques for better AI-generated code

Executive overview

AI coding tools produce inconsistent results without deliberate structure. Treating vibe coding like engineering — with plans, tests, and version control — yields dramatically better outcomes.

The best vibe coding practices are just good software engineering practices applied to LLM-driven workflows.

Choosing the right tool

• Non-coders: start with Lovable or Replit for visual, UI-first iteration.
• Experienced developers: jump straight to Cursor, Windsurf, or Claude Code.
• Running two tools (e.g. Cursor + Windsurf) in parallel on the same project lets you pick the better result.

Planning before coding

• Before writing any code, use the LLM to produce a comprehensive plan in a markdown file inside the project folder.
• Mark features explicitly as out of scope to prevent scope creep.
• Implement the plan section by section; check each section works before moving on.
• Commit to git after each working section so you can revert cleanly.

Version control is non-negotiable

• Use git religiously — don't rely on built-in revert tools in the IDE.
• Start each new feature from a clean git state.
• After multiple failed prompts, git reset --hard and feed the final working solution back into the LLM on a clean base.
• Accumulating failed attempts produces layered junk code; resetting avoids this.

Writing tests

• Get the LLM to write tests, but push for high-level integration tests over unit tests.
• Simulate end-to-end user flows rather than testing individual functions.
• Tests catch regressions when the LLM silently modifies unrelated logic.
• Write tests before moving to the next feature.

Fixing bugs

• Paste error messages directly into the LLM — no explanation needed.
• For complex bugs, ask the LLM to propose three or four possible causes before writing any fix.
• Reset after each failed fix attempt; do not let fix attempts accumulate.
• Add logging when stuck. Switch models if one fails — different models succeed where others don't.
• Once the root cause is found, reset all changes and give a clean, precise fix instruction.

LLM instruction files

• Write explicit instruction files (cursor rules, windsurf rules, CLAUDE.md, etc.).
• Detailed instructions make the agent significantly more consistent and effective.

Documentation

• Download API docs locally into a project subdirectory rather than pointing the agent at live URLs.
• Instruct the LLM to read docs before implementing anything that depends on them.

Complex new features

• Build complex features as a standalone project in a clean code base first.
• Get a small reference implementation working, then point the LLM at it when integrating into the main project.

Architecture and tech stack

• Prefer modular, service-based architecture with clear API boundaries — easier for both humans and LLMs.
• Avoid massive monorepos with deep interdependencies.
• Mature frameworks with large training datasets (e.g. Rails) produce more reliable LLM output than niche languages.

Useful extras

• Paste screenshots directly into the coding agent to demonstrate UI bugs or share design inspiration.
• Use voice input (e.g. Aqua) to dictate at ~140 wpm — LLMs tolerate imperfect transcription well.
• Refactor frequently once tests are in place; ask the LLM to identify repetitive code.
• LLMs handle non-coding DevOps tasks (DNS, hosting setup, image resizing) well — use them for that too.
• Keep experimenting: model rankings shift weekly; test new releases across different task types.