VIBE CODINGGUIDE

Use vibe coding when speed, experimentation, and rapid feedback matter most. It shines when you can tolerate iteration and rework while exploring a product surface or validating assumptions. The cost of a wrong turn is low, and the learning value is high.

Shift to traditional or hybrid workflows when reliability, security, or long-term maintainability become the priority. The higher the blast radius, the more you should design before you generate. Think of vibe coding as a generator, not the final authority. If requirements are unclear, slow down and clarify before you prompt.

• Risk tolerance: Can you safely roll back if the change is wrong?
• Scope clarity: Do you know the boundaries and acceptance checks?
• Lifecycle: Is this a short-lived experiment or a long-term system?

This loop is the operational backbone of vibe coding: a tight cycle of intent, generation, and verification. Treat each pass as a hypothesis. If the model proposes a change, you prove or reject it quickly. The loop is not a brainstorm; it is a narrowing funnel.

Keep cycles short and scope frozen. When the model starts expanding the spec, reset the prompt or slice the work smaller. Exit the loop only when behavior, UX, and test signals converge, not just when the model looks right. Track what changed each pass to prevent drift.

• Exit signals: readable diff, tests pass, UX matches, edge cases sanity-checked.

High-quality prompts are compact specs: they define the outcome, draw boundaries, and include a success checklist. When you encode constraints (stack, style, non-goals), the model stops guessing and starts aligning. The prompt is part of your engineering system, so treat it like a design doc that can be reviewed.

Provide just enough context to anchor the model in reality: key files, naming conventions, and what must not change. When a change is risky, ask for a plan first, then approve a smaller, explicit diff. You are steering intent, not filling in prose. If you cannot measure success, make the task smaller.

• Include: goal, constraints, non-goals, inputs/outputs, acceptance checks.

A vibe check catches obvious issues, but it cannot prove correctness. Quality gates convert subjective confidence into measurable validation. Think of them as deliberate friction: each gate forces a pause before you scale the change.

Order matters. Read the diff before running tests so you know exactly what changed. Then run tests, manually check the UX, and probe edge cases. If a gate fails, fix the root cause and rerun the gate instead of stacking more changes. Gates should scale with risk, not with ego.

• Minimum bar: diff review, local run, one edge-case check.

Debugging with AI works best when you force clarity. Always reproduce the bug, isolate the failing file or function, and ask for a targeted fix. The model is strongest when the problem surface is small and the evidence is concrete.

Large rewrites feel productive but often hide regressions. Ask for a minimal patch, apply it, and rerun the repro. If you are stuck, roll back and reframe the prompt with new evidence instead of piling on guesses. Anchor the fix to a failing test or log snippet.

• Bring: repro steps, expected vs actual, error logs, environment details.

Workflow hygiene is how you keep velocity without chaos. These habits preserve context, protect against regressions, and make it easy to hand off work to another human or another model. Hygiene is the difference between “fast” and “fragile.”

Treat prompts as artifacts: store them, name them, and connect them to commits. Keep diffs small and commit often so you can unwind mistakes without losing hours. If you only adopt a few habits, start with commits and prompt logs. Timebox sessions to avoid context sprawl.

• High ROI: prompt log, tight branch scope, frequent rollback points.

Tooling should reflect the stage of work. Use fast editors and browser agents to explore, then bring in CLI agents and repo search to verify, refactor, and harden. Keep the model close to real files and real constraints to avoid hallucinated gaps.

The best stack minimizes translation between idea, code, and validation. If a tool cannot read the repo or run the code, it will drift. Bias toward tools that can touch the same files you will ship. Avoid tools that only summarize without file access.

• Pick by need: UI feel, repo-wide changes, or verification speed.

Recipes are repeatable prompt structures that reduce variance and improve reliability. They let you express goals consistently while keeping scope tight and reviewable. If your team uses the same recipe, review becomes faster and more predictable.

Every recipe should be a template with slots for goal, constraints, files, and acceptance checks. Never paste a recipe blindly; adapt it to the repo rules and the specific change you want. The recipe is a scaffold, not a straightjacket. Retire recipes that drift or cause rework.

• Structure: context, task, constraints, outputs, verification.

AI accelerates output, but teams still need accountability. Assign a clear owner for requirements, a builder for the prompt loop, and a reviewer who validates behavior and risk. This separation prevents model drift from turning into production debt.

Shared standards matter. Use a consistent prompt format and a consistent review checklist so everyone evaluates with the same bar. Rotate reviewers to reduce blind spots and keep quality expectations aligned across the team. Document decisions so constraints are visible later.

• Roles: Owner sets scope, Builder runs loop, Reviewer verifies.

AI is a powerful collaborator, not a trusted authority. You are responsible for security, privacy, and compliance, especially around data and dependencies. Treat model output like untrusted input until it is verified.

Classify data before you paste it. Use least-privilege tokens, audit what changes, and keep a rollback path. If the model touches dependencies, pin versions and review changelogs. If a tool reaches production data, add explicit red-team checks.

• Guardrails: no secrets in prompts, dependency pinning, change logs, backups.

This checklist is the release brake at the end of every iteration. It ensures you have validated scope, reviewed changes, and captured follow-ups before you move on. Momentum is great, but it should never skip verification.

Use it even when the change feels tiny; most regressions come from “small” edits. Check the diff, run a test, sanity-check the UX, and record what is next. A short pause here saves hours later. After merge, capture follow-ups so the next loop starts clean.

• Before done: diff review, tests, UX sanity, docs, follow-ups.