Vibe Coding Prompting Best Practices for Codex, Claude, Gemini

AI has moved far past basic autocomplete. Today, it can act like a real coding partner. With vibe coding, you explain what you want in plain language, and the AI builds the first version for you. That can speed things up fast. But your results still depend on two things: how clearly you write your prompt, and how well you set boundaries.

In 2026, the big three tools are Codex (OpenAI), Claude (Anthropic), and Gemini (Google). They’re all powerful, but they don’t think or respond the same way.

In this guide, I’ll share prompt habits that consistently work, along with practical tips to help you get clean, reliable output from each one.

Why Vibe Coding Promp Engineering Matters?

AI coding assistants behave like talented junior developers. They understand frameworks and patterns, but they lack your project’s history and domain constraints.

If your instructions are vague—“Build me a login page”—you’ll get generic boilerplate. A precise prompt such as “Create a login form in React using Tailwind connected to Supabase Auth, with error handling for expired tokens and social login options” gives the model the context it needs to integrate properly.

Models are also non‑deterministic: ambiguous prompts magnify randomness. Treat your AI like a colleague; keep the tone professional and minimise ambiguity.

Successful vibe coders weave context into every prompt. Large language models reset at the start of each session, so you must reintroduce vital information project goals, technology stack, and constraints rather than assuming the model “just knows”.

The same communication skills used with human team-mates apply: provide background, clarify objectives, and set acceptance criteria.

Building Better Prompts: Core Principles

1. Define the persona and problem

Before requesting code, define who the AI should emulate and what you want. Ran Isenberg suggests starting with a role: “You’re a senior AWS Serverless Python developer specialising in secure multi‑tenant SaaS solutions”.

Next, state the task clearly, including functional and non-functional requirements and any libraries or folders involved. Positivity helps tell the model what you expect rather than what you don’t.

2. Provide context and references

A good prompt includes links to relevant documentation and examples. For instance, point Claude to the correlation‑id library on GitHub or your organisation’s style guide. If using Gemini, prime the model by instructing it to read specific docs before coding.

When your project uses multiple agents (Codex CLI, Claude Code, Gemini CLI), store persistent context in files like CLAUDE.md and AGENTS.md so each agent can read conventions on startup. This approach gives AI assistants a memory between sessions without leaking secrets.

3. Structure your vibe coding prompt

Supabase’s research highlights a three‑layer prompt structure:

1. Technical context and constraints– specify the stack (framework, language, styling), architectural patterns, and any naming conventions.
2. Functional requirements– describe the feature from the user’s perspective, including behaviours and interactions.
3. Integration and edge cases– explain how the code connects with your existing application and handle real‑world scenarios (error handling, authentication, rate limiting, external API calls).

This layered structure eliminates guesswork and yields production‑ready code on the first attempt. You can adapt it for data models, API endpoints or UI components; Supabase provides templates that include fields for performance, security and testing.

4. Be specific and sequential

Ask for one task at a time rather than bundling five unrelated requests. Detail high‑level to‑dos and, optionally, not‑to‑dos—negative constraints reduce ambiguity.

Provide mockups or sample data if the UI matters, and use “act as” framing when helpful (e.g., “act as a UX researcher”), but avoid slang.

List steps in order; for example, instruct the AI to write tests before coding or list assumptions, plan and risks before making changes.

5. Ask for a plan first

Complex tasks benefit from planning. Before the AI writes code, request a plan or list of assumptions. This lets you correct misunderstandings early. If the model appears stuck or generates poor results, start a fresh session and explicitly tell it to avoid the failed approach.

6. Leverage models’ strengths and switch when needed

Each tool has unique capabilities:

• Codex (OpenAI GPT‑5) delivers high‑quality Python and TypeScript code with strong reasoning. It supports tool usage (shell, code execution) and benefits from sequential instructions. When using Codex CLI, maintain context files and commit often; the CLI will ask for confirmation before executing commands.
• Claude Code (Opus 4.6) shines with long context windows and refined instruction following. Anthropic’s docs recommend clear system roles (“You are a helpful coding assistant specialising in Python”), explicit output formatting, and use of XML tags to structure instructions. Claude responds well to examples wrapped in <example> tags.
• Gemini (Google) handles multi‑modal inputs and excels when you provide images, diagrams or UI screenshots. Its CLI encourages context engineering: ask it to read documentation or run go doc before coding. Gemini’s models (3 Pro, 2.5 Flash) are designed to think stepwise when you request it to “brainstorm” or “think through” a problem.

Don’t hesitate to switch models if one gets stuck; different reasoning styles can unlock solutions.

Managing Context and Iteration

Vibe coding is an iterative conversation. Treat the AI’s output as a rough draft and refine it through cycles: Prompt → Review → Ask for explanation/refactor → Build next step.

Encourage the model to critique its code before you accept it—ask “What could go wrong?” or “Review this code as if it’s going live tomorrow; identify any issues”.

Use follow‑up prompts to probe security (e.g., “What security best practices should I follow?”) and performance concerns. Maintaining context across sessions is essential.

Use long context windows when available and store ongoing knowledge in files (CLAUDE.md, AGENTS.md, MEMORY.md, etc.) that your AI can read at startup. Periodically clear context to save tokens and avoid contamination when starting unrelated tasks.

Debugging, Testing, and Security

AI often generates code that compiles but fails at runtime. When errors occur, paste the error and relevant code into the prompt and ask the model to explain what went wrong.

If the error persists, have the AI list possible causes or add logging to narrow down the issue. Tools like Playwright can automate browser debugging; instruct the AI to install and use them when appropriate.

Testing is non‑negotiable. Ask the AI to write end‑to‑end or unit tests immediately after generating a feature. Consider a test‑driven approach: request a failing test first, then implement the code and iterate.

Once tests exist, refactor regularly to improve design and maintainability. Similarly, commit changes often with clear messages; you can even ask the AI to draft commit summaries explaining what changed and why.

Security requires explicit attention. Ask the AI to perform a security audit of the application, and always forbid hard‑coded secrets—use environment variables instead.

Supabase’s security‑focused iteration suggests directly prompting about security considerations; this surfaces recommendations like rate limiting, input validation and using secret storage. When designing prompts for API endpoints or data models, include fields for access control and sensitive data handling.

Guardrails and Human Oversight

Vibe coding is powerful but can lead to technical debt if left unchecked. Establish standards early: decide on architecture patterns, naming conventions and code style, then review the AI’s output to enforce them.

Schedule refactoring sessions; AI tends to append code and skip cleanup, so periodically instruct it to break modules into smaller files and remove dead code.

Remember that AI is a tool, not a replacement for engineering judgment. Always review, refactor and test what the model produces. Over‑reliance dulls your skills and can introduce hidden bugs or security flaws.

If a prompt session isn’t working, start fresh and give the model new instructions. Keep prompts simple; ask the agent to simplify code and remove unnecessary abstractions.

Putting It All Together: Example Prompt

The following example illustrates a well‑structured prompt for an API endpoint using the three‑layer approach. It targets Claude Code but works with Codex and Gemini as well:

<system>
You are a senior TypeScript developer specialising in secure REST APIs.</system>
<instructions>
Create an Express.js POST endpoint to create a new user record.  Follow these guidelines:
</instructions>
<context>
• Framework: Node.js with Express 5
• Authentication: JWT using Supabase Auth
• Data layer: PostgreSQL via Prisma ORM
• Response format: JSON with a `status` field and a `data` object
</context>
<functional_requirements>
• Validate that `email` and `password` are provided in the request body
• Hash the password using bcrypt before storing
• Return the newly created user’s `id`, `email` and `createdAt`
</functional_requirements>
<integration_and_edge_cases>
• Handle unique‑constraint errors if the email is already taken
• Rate‑limit the endpoint to 5 requests per minute per IP
• On error, return an appropriate HTTP status and message
</integration_and_edge_cases>
<tasks>
1. List your assumptions, the plan and any potential risks before coding.
2. Write unit tests using Jest for success and failure scenarios.
3. Implement the endpoint following the plan and run the tests.
</tasks>
<formatting>
Respond with a numbered plan followed by the Jest tests in a single code block and then the implementation code in a separate code block.  Do not include any explanations outside of code.</formatting>

This prompt defines the role, supplies technical context, specifies functional and edge‑case requirements, asks for a plan and tests, and controls the response format.

You can adapt the tags to your model (XML tags are optional but helpful for Claude) and extend the context with links to your organisation’s docs.

Frequently Asked Questions (FAQs)

What is vibe coding?

Vibe coding refers to building software by describing what you want in natural language to an AI coding assistant, which then generates code. The developer guides, tests and refines the AI’s output rather than writing every line manually.

How do I write effective prompts for Codex, Claude, or Gemini?

Start with a clear persona and problem statement, provide technical context (frameworks, libraries, constraints), describe functional requirements and edge cases, and request a plan before coding. Use examples and links to documentation when possible.

Why should I ask AI to write tests?

AI models tend to produce implementation‑first code with minimal test coverage. By asking for unit or end‑to‑end tests, you catch bugs early and encourage better design. A test‑driven approach leads to more stable products.

How do I maintain context across sessions?

AI assistants forget previous chats. Store persistent context in files such as CLAUDE.md or AGENTS.md and instruct the agent to read them at startup. For unrelated tasks, clear the context or start a new session to avoid contamination.

What security precautions are necessary when vibe coding?

Always perform a security audit, avoid hard‑coding secrets, use environment variables, implement rate limiting, validate inputs, and handle errors explicitly. Ask the AI to suggest security best practices for each feature.