TL;DR
If you want better code from Claude, start with a better prompt. Be explicit, structure your context, let Claude refine the prompt itself, and cut token-wasting fluff from responses.
Why Prompt Quality Matters in Vibe Coding
In vibe coding, how you prompt is everything. The model doesn’t guess intent well — it follows instructions.
The model mirrors your input: unclear prompts lead to unclear code.
This article outlines a practical, repeatable method to improve Claude’s output quality.
Step 1: Prepare a detailed Raw Prompt
Before “prompt engineering”, you need a solid base.
The goal is simple: remove ambiguity.
Write down everything you would normally explain to a teammate:
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Why the product exists
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What problem it solves
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How users move through it
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What features are required
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What constraints exist
Be specific about UI, behavior, and tools. Claude performs best when it doesn’t have to infer intent.
A Simple Prompt Structure
Use this structure every time you start:
Why? What problem does this solve?
User flows How does a user move through the product?
Features What must the product do?
Tech stack Frameworks, libraries, platforms, constraints.
Edge cases / exceptions Failure states, unusual inputs, limits.
This is a one-time process. It takes longer at first, but saves far more time later.
Step 2: Improve the Prompt with Prompt Engineering
By definition_, Prompt engineering_ means deliberately shaping instructions so a model produces better output. For example, instead of “build a login page,” you specify validation rules, error states, and UX constraints.
Most of us stop at the definition.
A more effective approach is simpler: ask the model to improve your prompt.
After some experimentation, I found the following meta-prompt which works well across LLMs.
Meta-Prompt to Refine Your Prompt
Act as an expert prompt engineer.
Your task is to enhance the following prompt to the highest level so that it instructs
<LLM: Claude Code CLI / Cursor / ChatGPT>
(model: <Claude Opus / GPT-5.2>)
to produce high-quality code like a senior software engineer.
Be specific.
Apply prompt-engineering best practices.
Use official guidance from the LLM provider where relevant.
Here is my prompt:
Then paste your raw prompt below it.
Why This Works
Claude understands Claude’s own strengths and constraints better than other models do. Let it optimize instructions for itself.
As a rule of thumb:
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Claude → refine prompts for Claude
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ChatGPT → refine prompts for ChatGPT
Step 3: Control Response Style to Avoid Token Waste
Most LLMs add unnecessary text:
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Success messages like “✅ Task completed successfully”
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Explanations you didn’t ask for
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Summaries after every task
Most of the time, this is unnecessary and consumes extra tokens.
You can prevent this by explicitly defining how responses should look.
Example: Minimal Response Rules for Claude Code
Add something like this to your CLAUDE.md
<response_style>
## Communication Rules
1. No victory laps
Do not announce completion with celebratory messages.
2. No unsolicited explanations
Do not explain changes unless explicitly asked.
3. No teaching unless requested
Avoid educational commentary or advice.
4. Minimal confirmations
Use "Done." or proceed silently.
5. No summaries unless asked
Complete the task and stop.
## When to provide detail
- When asked to explain or summarize
- When clarification is required
- When reporting blocking errors
- When presenting a plan for approval
## Response format
- Actions → minimal commentary
- Errors → brief, actionable
- Questions → concise, wait for reply
- Plans → structured list, then pause
</response_style>
This alone can significantly reduce noise and token usage.
Common Mistakes to Avoid
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Writing prompts like feature requests instead of specs
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Letting the model guess edge cases
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Accepting verbose responses by default
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Treating prompt quality as a one-time task
Prompting is iterative. Treat it like code.
Final Thoughts
Better prompts produce better code — consistently.
If you:
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Write explicit, structured raw prompts
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Let Claude refine them
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Enforce a clean response style
You’ll get concise, cleaner, and reliable results from vibe coding.
If this helped, save the prompt templates and reuse them. Your future self will thank you.