Every AI Coding Platform Has a Prompting Guide, You Only Need One

The universal framework that works on Claude, Cursor, Bolt, Lovable, and everything else

Have you noticed that every AI coding platform has its own prompting guide?

Lovable has their playbook.
Bolt has their best practices.
OpenAI and Claude have entire GitHub repos.
Cursor, Replit, GitHub Copilot, v0, Gemini… they all publish complete documentation on “the right way” to prompt.

But do we really need to learn all of that? Or is there something universal underneath?

I spent months finding out.

I read all guides I encounter. Compared them. Looked for patterns.

What I found surprised me.

Most prompt engineering advice is overcomplicated. The platforms emphasize their unique features, but when you strip away the marketing, they’re all telling you the same thing in different words.

That means you don’t need to learn nine different prompting systems. You need to master one and adapt as you go.

In this article, you’ll learn:

• The universal prompting framework that works across every AI coding platform

• What’s truly unique to each platform (and what’s just branding)

• The 5 techniques that matter most — and the 10 habits to drop

• How to adapt your prompt for Bolt vs Claude vs Cursor without rewriting everything

• Your action plan based on skill level (beginner → advanced)

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The Myth of Platform-Specific Prompting

This is what the documentation tells you:

Claude: “Use XML tags like <instructions> and <example> for better results”

Replit: “Structure prompts with checkpoints for incremental building”

Cursor: “Create rules files and use @-mentions for context”

Bolt: “Keep it natural - and use the optional prompt enhancement if needed”

Sounds like you need completely different prompts for each platform, right?

But when I tested the same prompt across all different platforms:

I want to build a spending tracker app.
  
Features:  
- Log daily expenses with categories  
- See monthly spending summaries  
- Budget alerts when overspending
  
Tech: React, TypeScript  
Keep it simple - just for personal use.  

Every platform produced working code. No XML tags. No checkpoints. No special formatting. Just clear, natural language describing what I wanted.

The specialized formats aren’t required. They’re optimizations. Small improvements for edge cases. Nice-to-have, not need-to-have.

The truth most don’t want to admit is: Modern LLMs are remarkably robust. They understand intent regardless of format. A well-structured plain English prompt beats a poorly-thought-out XML-tagged prompt every time.

But that doesn’t mean format doesn’t matter at all. Let me show you what actually makes a difference.

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The Universal Framework That Actually Works

After analyzing documentation from all nine platforms, I found they all converge on the same core principles. They just describe them differently.

The Shared Foundation

Every platform recommends the same basic structure:

1. WHAT you want to build (goal)  
2. WHY you're building it (context)  
3. HOW it should work (requirements)  
4. WHAT to avoid (constraints)  

That’s it. Four elements. Everything else is decoration.

GitHub Copilot describes it as a context-gathering pipeline.

Claude frames it as “Be clear and direct” and “Let Claude think.”

OpenAI structures it as “Identity, Instructions, Examples, Context.”

Replit teaches principles like “Specify,” “Select,” and “Checkpoint.”

Same ideas. Different vocabulary.

The Universal Prompt Template

The template that works everywhere:

I want to build [WHAT - one sentence description].
  
Context:  
- [WHY - what problem this solves]  
- [WHO - who will use it]  
- [WHERE - deployment environment]
  
Requirements:  
- [MUST HAVE - core features]  
- [NICE TO HAVE - optional features]
  
Technical:  
- [STACK - languages/frameworks]  
- [CONSTRAINTS - what to avoid]
  
Start with [FIRST STEP - where to begin].  

Example:

I want to build a personal spending tracker.
  
Context:  
- I'm drowning in receipts and have no idea where my money goes  
- Just for me, no multi-user needed  
- Will deploy on Vercel
  
Requirements:  
- Log expenses with amount, category, and date  
- See monthly totals by category  
- Alert when spending exceeds budget
  
Technical:  
- React + TypeScript + Tailwind  
- Use localStorage for MVP (no backend yet)  
- Keep dependencies minimal
  
Start with the data model and expense entry form.  

This prompt works on Claude, ChatGPT, Cursor, Replit, Bolt, Lovable, v0… all of them. No modifications needed.

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What’s Unique to Each Platform

Now let’s talk about what’s genuinely different. Not marketing differences. Real architectural differences that affect how you should work.

Tier 1: Fundamentally Different Interaction Formats

GitHub Copilot - Inline completion, not chat

• Prompts through code comments

• Context from open files only

• Tab-accept workflow

• When to use: You’re already coding and want suggestions

Cursor - IDE with codebase awareness

• @-mentions pull specific files

• Rules files persist across sessions

• Applies diffs, not full rewrites

• When to use: Working in an existing codebase

• Deep dive: How AI Builders Use Cursor: The Complete Guide

v0 - UI-focused with full-stack capabilities

• Outputs React components with shadcn/ui

• Supports Next.js backends and databases (Supabase, Neon)

• Design-to-code focused

• When to use: You need UI components fast, with optional backend

Tier 2: Different Default Behaviors

Bolt - Offers optional prompt enhancement

• Clear, detailed prompts work best

• Asks clarifying questions

• WebContainer environment (browser-only)

• Unique behavior: Click “Enhance prompt” for AI-suggested improvements

Replit - Full-stack by default

• Assumes you want frontend + backend + database

• Built-in deployment

• Checkpoint-friendly architecture

• Unique behavior: Thinks in deployable units

Lovable - Visual-first, Supabase-native

• Prioritizes design over logic

• Native Supabase integration

• Screen-by-screen thinking

• Unique behavior: Starts from how it looks

Tier 3: Similar Under the Hood

Claude, ChatGPT, Gemini - General-purpose assistants

• All understand the same prompt formats

• All support markdown, code blocks, structured output

• Minor differences in reasoning style

• The truth: Interchangeable for 90% of coding tasks

So if the platforms are more alike than different, what genuinely matters? The techniques that work everywhere.

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The 5 Techniques That Matter Most

After analyzing what each platform recommends, these are the techniques that consistently improve output across all platforms:

1. State the WHY, Not Just the WHAT

Every platform documentation mentions this: OpenAI and Claude both call it “context,” while Replit addresses it through their “Select” principle.

Weak:

Create a login function  

Strong:

Create a login function.
  
Context: We're seeing high support ticket volume from users  
who forget their passwords. We want to reduce friction  
while maintaining security. This is a B2B SaaS where  
users log in once per week on average.  

The WHY changes the HOW. Without it, AI makes assumptions. With it, AI makes informed decisions.

2. Set Clear Boundaries

Every platform recommends defining what’s out of scope, but most prompts skip this entirely.

OpenAI: “Specify constraints”

Claude: “Give Claude an out” for uncertain situations

Replit: “Specify” principle, define necessary constraints

Gemini: “Specify any constraints on the response”

The key is defining boundaries around scope, dependencies, and responsibilities, what’s already handled elsewhere, what you’ll add later, what the AI shouldn’t touch.

Example:

Build a user dashboard.
  
Boundaries:  
- Authentication is handled elsewhere (don't add it)  
- Use existing /api/user endpoint (don't create new ones)  
- No new dependencies  
- Skip loading states for now (we'll add later)  

Clear boundaries prevent scope creep and reduce AI’s tendency to over-engineer.

3. Show One Example, Not Ten

Every platform talks about examples. But they disagree on quantity.

Research across platforms shows:

• 0 examples (zero-shot): Works for simple, well-defined tasks

• 1-2 examples: Best for format/style guidance

• A few examples: Experiment to find optimal number (varies by task)

• Too many examples: Diminishing returns, risks overfitting

The rule: Show the minimum examples needed to communicate the pattern. More isn’t better.

Example for API response format:

Return responses in this format:
  
{  
  "success": true,  
  "data": { ... },  
  "error": null  
}
  
One example is enough - don't need ten variations.  

4. Ask for Reasoning Before Code

This technique appears under different names:

• Claude: “Ask Claude to think step by step”

• OpenAI: “Chain-of-thought prompting” (note: not recommended for reasoning models like o1/o3)

• Gemini: “Step-by-step reasoning”

The pattern:

Before writing any code:  
1. Explain your architectural approach  
2. Identify potential edge cases  
3. List any assumptions you're making
  
Then implement.  

This catches misunderstandings BEFORE you have 200 lines of wrong code to debug.

See this technique applied to a real project with the 7-Round Conversation System in AI Digest App Building Guide.

5. Constrain the Output Format

All platforms improve with explicit output instructions.

Instead of:

Write a function to validate emails  

Use:

Write a function to validate emails.
  
Output format:  
- TypeScript  
- Single function, no classes  
- Return { valid: boolean, reason?: string }  
- Include JSDoc comments  
- No external dependencies  

Explicit format = consistent results.

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The 10 Things You Can Stop Doing

Based on what the research actually shows (vs. what gets repeated online):

1. Stop Using “You are an expert...”

The myth: Role prompts dramatically improve output.

The reality: Modern LLMs already have expertise. Roles add marginal improvement for specific domains, nothing for general coding.

Instead of:

You are an expert senior software architect with 20 years  
of experience in distributed systems...  

Just say:

Design a caching layer for our API.  

The uncomfortable truth is: highly specific roles can actually limit the output. Tell the AI it’s a “senior architect” and it might over-engineer. Tell it to think like a “junior developer” and it might under-engineer. Sometimes no role produces the most balanced result.

I’ve tested this repeatedly, I get better code from Claude with no role than from Claude told to be a “senior engineer.”

2. Stop Over-Structuring Simple Tasks

The myth: More structure = better results.

The reality: Over-specification limits AI’s useful contributions.

Don’t do this for a simple function:

<context>  
  <project>My app</project>  
  <language>TypeScript</language>  
</context>  
<requirements>  
  <requirement id="1">Validate email</requirement>  
</requirements>  
<constraints>  
  <constraint>No regex</constraint>  
</constraints>  

Just say:

Write a TypeScript function to validate email addresses  
without using regex.  

3. Stop Giving “Don’t” Instructions

Research from multiple platforms shows: Positive instructions are more effective than negative instructions. Claude’s docs explicitly state: “Tell Claude what to do instead of what not to do.”

Weak:

Don't use deprecated APIs  
Don't write comments  
Don't create helper functions  

Strong:

Use current/stable APIs only  
Write self-documenting code without comments  
Keep logic in a single function  

Negative instructions still activate the unwanted pattern in the model’s attention.

4. Stop Dumping Your Entire Codebase

The myth: More context is always better.

The reality: Irrelevant context decreases accuracy.

Only include:

• Files directly involved in the change

• Interfaces/types that will be used

• Examples of existing patterns to follow

Don’t include:

• Unrelated files “for reference”

• Entire folders “just in case”

• README files unless relevant

5. Stop Asking for “Complete” Solutions

The myth: Ask for everything at once to save time.

The reality: “Give me everything” produces shallow coverage of many things.

Weak:

Build a complete user management system with registration,  
login, password reset, email verification, role-based  
access control, audit logging, and admin dashboard.  

Strong:

Build user registration with email verification.
  
We'll add login, password reset, and other features  
in follow-up prompts.  

6. Stop Rewriting Prompts for Each Platform

If your basic prompt is clear, it works everywhere. Only add platform-specific elements when you’re hitting specific limitations.

7. Stop Using Emoji Instructions

The myth: Emojis make prompts clearer.

The reality: No evidence they help. Some evidence they confuse.

8. Stop Specifying Temperature/Model Settings

Unless you’re building systems, let the platform choose. Their defaults are optimized.

9. Stop Asking AI to “Be Creative”

Creativity instructions are too vague. If you want specific creative approaches, describe them.

10. Stop Optimizing Prompts Before Testing

Write a simple prompt. Test it. Only optimize if it fails. Premature prompt optimization is the root of much wasted time.

Now that you know what to stop doing, let’s get practical. When does platform-specific adaptation help?

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Platform Adaptation: Same Prompt, Different Wrappers

How to adapt the same core prompt for different platforms when you need specific optimizations:

The Core Prompt

Build a spending tracker app.  
- Log expenses with category and amount  
- Show monthly summaries  
- Budget alerts at 80% spent  
Tech: React, TypeScript, localStorage  

Bolt Adaptation (Keep it Short)

Build a spending tracker where I can:  
- Log daily expenses with categories  
- See how much I'm spending per month  
- Get warned if I'm over budget
  
Make it look clean and modern.  

Why: Bolt offers optional prompt enhancement. Keep prompts clear and let it ask follow-up questions.

Replit Adaptation (Add Checkpoints)

Build a spending tracker app.
  
CHECKPOINT 1: Basic expense form  
- Amount, category dropdown, date picker  
- Save to localStorage
  
CHECKPOINT 2: Expense list  
- Show all expenses  
- Filter by date range
  
CHECKPOINT 3: Dashboard  
- Monthly totals  
- Category breakdown
  
Start with Checkpoint 1.  

Why: Replit thinks in deployable increments. Checkpoints match their mental model.

Cursor Adaptation (Reference Codebase)

Looking at the existing patterns in @src/components,  
add a SpendingTracker feature:
  
- Match the form style in @src/components/ExampleForm.tsx  
- Follow the hook pattern in @src/hooks/useLocalStorage.ts  
- Store expenses in localStorage
  
Start with the data types and useExpenses hook.  

Why: Cursor shines with codebase context. Reference existing files.

Lovable Adaptation (Visual-First)

Create a beautiful spending tracker.
  
DESIGN:  
- Clean, minimal cards  
- Soft shadows, rounded corners  
- Blue accent, red for warnings  
- Mobile-first
  
SCREENS:  
1. Dashboard - big "Spent this month" number, category cards  
2. Add Expense - modal with large amount input  
3. History - list grouped by date
  
Use Supabase for data.  

Why: Lovable thinks in screens and visuals. Design language matters.

Claude/ChatGPT Adaptation (Add Reasoning)

I want to build a personal spending tracker.
  
Context:  
- Just for me, no auth needed  
- Want to understand my spending patterns  
- Will run locally, no server
  
Requirements:  
- Log expenses (amount, category, date, note)  
- Monthly summary with category breakdown  
- Alert when category exceeds budget
  
Before coding, explain:  
1. Your data model choices  
2. How you'll handle the budget alerts  
3. Any edge cases to consider  

Why: General assistants benefit from explicit reasoning requests.

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Wrapping Up

Your Action Plan

If You’re Just Starting:

Use the universal template:

I want to build [WHAT].
  
Context:  
- [WHY]  
- [WHO]  
- [WHERE]
  
Requirements:  
- [MUST HAVE]  
- [NICE TO HAVE]
  
Technical:  
- [STACK]  
- [CONSTRAINTS]
  
Start with [FIRST STEP].  

This works on every platform. Don’t overcomplicate it.

If You’re Intermediate:

Add these techniques:

1. Always state WHY, not just WHAT

2. Include explicit boundaries (what’s out of scope)

3. Ask for reasoning before code

4. Use 1-2 examples for format guidance

5. Constrain output format explicitly

If You’re Advanced:

Focus on:

1. Context curation (what to include vs exclude)

2. Strategic decomposition (breaking complex tasks)

3. Platform-specific optimizations when hitting limits

4. Building reusable prompt components (rules files, templates)

5. Knowing when to NOT use AI

The Meta-Skill:

The best prompt engineers don’t just write good prompts. They know WHEN to prompt (vs when to code directly). They know WHAT to prompt for (vs what AI will struggle with). They know WHETHER to trust the output (vs when to verify carefully). Prompting is one skill in a larger system of AI-augmented development.

Quick Reference

PlatformBest ForUnique StrengthClaudeComplex reasoning, long contextThinking through problemsChatGPTGeneral purpose, pluginsEcosystem and integrationsCursorExisting codebases@-mentions and rules filesCopilotInline suggestionsFlow-state codingReplitFull-stack prototypesInstant deploymentBoltRapid MVPsOptional prompt enhancementLovableVisual appsDesign-first approachv0UI componentsshadcn/ui integrationGeminiGeneral purpose, multimodalMeta-prompting (”How can I improve this prompt?”)

Choose based on what you’re building, not which is “best.”

The Bottom Line

Prompt engineering isn’t magic. It’s communication.

Clear communication works everywhere. Unclear communication fails everywhere. The platforms have different features, but they all run on the same principle: Tell the AI what you want, why you want it, and what constraints apply.

Everything else is optimization.

Start simple. Test. Iterate. Don’t over-engineer your prompts before you’ve seen what a basic prompt produces. The goal isn’t the perfect prompt. The goal is working software.

Based on original research analyzing documentation from GitHub Copilot, Claude, OpenAI, Cursor, Replit, Bolt, Lovable, v0, and Gemini. All platform claims verified against official documentation as of January 2026.

Resources

The original source documentation

I’ve compiled all the platform guides into clean, readable markdown files — the same sources I used to write this article. Download free with coupon code btlprompt: Get the AI Prompting Docs Bundle

Try the AI Prompt Generator (for free)

Describe your app idea → get optimized prompts for any platform. 5 free generations for free users and unlimited generations for paid members.

👉 vibecoding.builders/custom-tools/prompt-generator

Related articles:

• How Two Prompting Strategies Made My AI Code Production-Ready

• Best Practices for AI-Powered Software Engineering

• The Essential Software Engineering Practices Every AI Builder Needs to Know

• How to Make Vibe Coding Production-Ready (Without Losing Your Mind)

• How She Orchestrated 37 Contributors and 461 Users Into an AI Learning Event — Elena Calvillo at Product’s firsthand Lovable prompting process: breaking big features into 4-5 smaller asks

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What prompting techniques have worked best for you?