Vibe Coding & AI‑Assisted Coding: A Complete Tutorial

Vibe coding—sometimes called agentic coding—is the practice of delegating entire software tasks (spec writing, scaffolding, testing, and even deployment) to an AI agent inside your IDE. You prompt at a high level and the agent iterates end‑to‑end.

AI‑assisted coding (IA coding) is broader: you still write most code yourself but rely on AI for suggestions, autocomplete, or quick fixes. Think GitHub Copilot or ChatGPT in a side panel.

Why Adopt Vibe Coding?

Key Advantages

• Speed & Throughput – Large blocks of boilerplate generated in minutes.

• Async Productivity – Launch a long AI iteration, switch to other work, return when done.

• Expanded Skill Surface – Let AI scaffold unfamiliar tech while you focus on product thinking.

• Built‑in Documentation – Agents can auto‑generate comments, READMEs, and tests.

Potential Drawbacks

• Reduced Control – The agent may pick unexpected patterns or libraries.

• Codebase Instability – Over‑eager refactors can break working features.

• Hidden Vulnerabilities – AI might miss security best practices.

• Shallow Solutions – “C‑student” answers to “A‑level” problems without tight prompts.

• Debugging Overhead – Tracing AI‑generated logic takes longer when errors surface.

Mitigation Strategy: Use strict Coding Pattern Preferences, an Abstract Technical Stack, and a disciplined Workflow (prompts provided below).

Core Prompts to Anchor Every Session

Paste each block at the start of a new AI chat. Replace placeholders (e.g., [YOUR DATABASE]) with your actual choices.

1. Coding Pattern Preferences

Enforce these coding patterns:
- Always prefer simple solutions.
- Avoid duplication: search existing code before adding new logic.
- Respect environments: dev, test, prod must stay isolated.
- Change only requested areas or code you fully understand.
- Exhaust current patterns before new tech; delete old code if replaced.
- Keep files ≤ [MAX_LINES] lines; refactor sooner if complex.
- Inline or delete one‑off scripts after use.
- Never mock data outside test scope.
- Never overwrite [YOUR ENV VAR FILE] without confirmation.
- Use [YOUR LOGGING LIBRARY] for structured logging.
- Follow naming: camelCase functions, UPPER_SNAKE constants.
- Document public functions with [YOUR DOC STANDARD].

2. Abstract Technical Stack

Base the project on:
- Backend: [YOUR BACKEND LANGUAGE] + [YOUR BACKEND FRAMEWORK]
- Frontend: [YOUR FRONTEND LIBRARY] + [YOUR CSS FRAMEWORK]
- Database: [YOUR DATABASE] (no file storage) with separate dev/test/prod schemas
- Search: [YOUR SEARCH SERVICE] hosted on [YOUR SEARCH HOST]
- Cache: [YOUR CACHE SERVICE]
- Queue: [YOUR MESSAGE QUEUE]
- Tests: [YOUR TEST FRAMEWORK]
- Static assets: [YOUR CDN OR STORAGE SERVICE]
- Secrets & config: [YOUR ENV VAR FILE] managed via [YOUR SECRET MANAGER]

3. Coding Workflow Preferences

Follow this workflow:
- Focus only on code relevant to [TASK DESCRIPTION].
- After coding, write end‑to‑end tests for [FEATURE NAME].
- Avoid architecture changes unless explicitly instructed.
- Consider side effects across modules before modifying code.
- Commit on green tests: “[TYPE]: [SHORT DESCRIPTION]”.
- Use feature branches “[TASK‑ID]-[SLUG]”, diff ≤ [MAX_DIFF_LINES] lines.
- Run CI: lint, format, build, test, security scan.
- If tests fail, fix them without altering unrelated logic.
- Start a fresh AI session for each major feature to limit context.

Step‑by‑Step Adoption Tutorial

Note: Steps reference the prompts above; ensure you paste them first.

Step 1 – Choose the Agent & Model

• Tool: [YOUR IDE OR TOOL]

• Model: [YOUR AI MODEL]

• API Key: [YOUR API KEY]

• Execution Mode: MANUAL, AUTO, or YOLO (unchecked for prod).

Step 2 – Load Coding Pattern Preferences

Paste the pattern prompt to bake consistency into the session.

Step 3 – Declare the Abstract Technical Stack

Paste the stack prompt so the agent scaffolds infrastructure correctly.

Step 4 – Generate a Feature Spec

Write a detailed spec for [FEATURE NAME] using the stack above.
Include entities, API endpoints, auth flow, and config variables.

Step 5 – Implement the Feature

Build [FEATURE NAME] according to the spec and rules.

Step 6 – Test & Validate

Agent writes and runs tests per workflow prompt. If tests fail:

Fix failing tests; do not modify unrelated code.

Step 7 – Commit & Merge

• Review diff for rogue patterns.

• Merge after CI passes.

Step 8 – Deploy & Monitor

• Trigger your pipeline.

• Use dashboards/logs from [YOUR MONITORING TOOL] to watch for regressions.

Debugging AI‑Generated Code

1. Re‑Prompt Narrowly – Ask the agent to explain rationale for a specific block.

2. Search for Duplicates – Run grep/IDE search to catch copy‑paste logic.

3. Insert Guard Tests – Reproduce failing cases as tests, then let AI fix.

4. Manual Review – Check third‑party calls, permissions, and error handling.

5. Static Analysis – Scan with [YOUR STATIC SCANNER] for vulnerabilities.

Security & Compliance Considerations

• Input Validation – Ensure AI adds sanitization around [YOUR INPUT SOURCES].

• Least‑Privilege Config – Hard‑code minimal DB roles in secrets manager.

• Dependency Hygiene – Force the agent to pin versions via [YOUR PACKAGE MANAGER] lockfiles.

• Audit Logs – Store agent commits separately for later review.

• Policy Enforcement – Lint for banned APIs or patterns using [YOUR POLICY TOOL].

Governance & Documentation

• Rule Files as Code – Check .rules/ directory into VCS for visibility.

• Prompt Library – Maintain a shared repository of approved prompts.

• Changelog Automation – Use the agent to draft release notes from merged PRs.

• Training & Onboarding – Pair newcomers with a curated tutorial session using these prompts.

Is Vibe Coding Worth the Hype?

Adopting vibe coding can 10× your output, but only if you balance automation with rigorous patterns, explicit stack definitions, and a disciplined workflow. Copy the three core prompts into every AI session, follow the step‑by‑step tutorial, and continuously refine your rules as the team matures. Done right, AI becomes an invisible teammate—handling repetitive tasks while you focus on architectural decisions, user experience, and innovation.

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