Update prompting/prompt-engineering-guide.mdx

Co-Authored-By: mintlify[bot] <109931778+mintlify[bot]@users.noreply.github.com>

Author: Gerome-Elassaad

prompting/prompt-engineering-guide.mdx

@@ -111,49 +111,48 @@ Build an online store dashboard with:
   **Start Simple**: Begin with a clear request, then add more details in follow-up messages.
 </Callout>
 
-## AI prompt engineering best practices summary
+## Quick Tips Summary
 
-### Essential AI prompting principles for code generation
+### The Main Rules
 
-1. **Clarity first**: Be specific about what you want to build with AI code generation and how it should work
-2. **Technology specification**: Clearly state your preferred frameworks and libraries for LLM understanding
-3. **Context provision**: Include relevant background information and constraints for better AI results
-4. **Iterative AI approach**: Start simple, then add complexity through follow-up prompts for optimal code generation
+1. **Be clear**: Say exactly what you want and how it should work
+2. **Name your tools**: Tell the AI which technologies to use
+3. **Give context**: Share relevant information and any limits
+4. **Start simple**: Begin with basics, then add details
 
-### AI code generation quality checklist
+### Checklist Before You Ask
 
-**Before submitting AI prompts:**
+**Before you send your request:**
 
-- ✅ Goal is clearly defined for LLM understanding
-- ✅ Technology stack is specified for AI code generation
-- ✅ Key features are listed for comprehensive AI development
-- ✅ User requirements are outlined for accurate LLM results
-- ✅ Success criteria are defined for measurable AI outcomes
+- ✅ You clearly explained your goal
+- ✅ You listed the tools you want to use
+- ✅ You mentioned the main features you need
+- ✅ You described what success looks like
 
-**During AI-powered development:**
+**While building:**
 
-- 🔄 Provide feedback on AI-generated code for improvement
-- 🔄 Request specific modifications from LLM for refinement
-- 🔄 Ask for AI explanations when needed for understanding
-- 🔄 Use AI discussion mode for architecture planning
+- 🔄 Give feedback on what the AI creates
+- 🔄 Ask for specific changes
+- 🔄 Request explanations if confused
+- 🔄 Use discussion mode to plan
 
-### Common Success Patterns
+### What Makes a Good Request
 
-**Effective Prompts Include:**
+**Good requests have:**
 
-- Specific functionality requirements
-- Technology stack preferences
-- User experience considerations
-- Performance and scalability needs
-- Integration requirements
+- Clear description of what you want
+- List of tools to use
+- How it should look and feel
+- Any speed or size requirements
+- How it connects to other things
 
-**Ineffective Prompts Lack:**
+**Bad requests are missing:**
 
-- Clear objectives
-- Technical specifications
-- Implementation details
-- Success criteria
+- A clear goal
+- Technical details
+- Specific instructions
+- Definition of "done"
 
 <Callout type="info">
-  **Remember**: LLMs can only generate code based on the information you provide in prompts. The more specific and complete your AI prompts, the better the code generation results and AI-powered development outcomes.
+  **Remember**: The AI can only work with what you tell it. The more details you give, the better the results.
 </Callout>