Prompt engineering has evolved from an art to a science. After analyzing over 1,000 prompts and their results across different models, patterns have emerged about what actually works in October 2025.
Here’s what you need to know.
The Biggest Myth: “Be Polite to AI”
You’ve probably heard advice like “Say please and thank you to the AI” or “Compliment it for better results.” I tested this extensively:
- Polite prompt: “Could you please help me refactor this code? Thank you so much!”
- Direct prompt: “Refactor this code to use async/await”
Result: Zero difference in output quality. Being polite doesn’t hurt, but it doesn’t help either. It just uses more tokens.
The AI doesn’t have feelings. It responds to instruction structure, not courtesy.
What Actually Matters
1. Specificity Beats Everything
Vague prompts get vague results. Compare:
❌ Bad: “Make this function better”
✅ Good: “Refactor this function to: 1) Use TypeScript strict mode, 2) Add error handling for network failures, 3) Extract the validation logic into a separate function”
The second prompt gets exactly what you want. The first gets… something.
2. Examples Are More Powerful Than Explanations
Instead of describing what you want, show examples:
❌ Weak: “Write in a casual, friendly tone”
✅ Strong: “Write in this style:
Example: ‘Hey! So you know how React hooks can be confusing? Let me break it down. Think of useState like a sticky note—it remembers stuff even when your component re-renders.’
Now write about useEffect in the same style.”
The AI matches the pattern you show it. This is called “few-shot prompting” and it’s incredibly effective.
3. Constraints Improve Quality
Paradoxically, adding constraints often makes output better:
- “Write a product description in exactly 50 words”
- “Explain this concept using only analogies a 10-year-old would understand”
- “Summarize in 3 bullet points, each under 15 words”
Without constraints, AI tends to be verbose and generic. Constraints force it to be creative and precise.
The Structure That Works
After testing hundreds of prompt variations, this structure consistently performs best:
1. ROLE (Who should the AI be?)
“You are an expert Python developer with 10 years of experience in data engineering”
2. TASK (What should it do?)
“Optimize this data pipeline for processing 1M records/hour”
3. CONTEXT (What info does it need?)
“Current bottleneck is the database writes. We’re using PostgreSQL. Memory is not a constraint but CPU is.”
4. FORMAT (How should the output look?)
“Provide: 1) The optimized code, 2) Explanation of changes, 3) Expected performance improvement”
5. CONSTRAINTS (What are the rules?)
“Don’t use external libraries beyond pandas and sqlalchemy. Code must be Python 3.11 compatible.”
Not every prompt needs all five, but this framework scales from simple to complex tasks.
Common Mistakes That Kill Prompt Quality
Mistake 1: Asking Multiple Questions in One Prompt
Don’t do this:
“Explain React hooks, compare them to class components, suggest when to use each, and show me how to build a custom hook for API calls”
The AI will try to answer everything but nothing deeply. Instead, ask four separate prompts or use chain-of-thought:
“First, explain React hooks in 2-3 paragraphs. Then I’ll ask follow-up questions.”
Mistake 2: Not Providing Context
The AI doesn’t know your codebase, your user’s needs, or your constraints unless you tell it.
| Without Context | With Context |
|---|---|
| “How do I handle auth?” | “How do I add auth to a Next.js 14 app using App Router? Users should log in with Google OAuth. We use Supabase for the database.” |
| “Fix this bug” | “This React component re-renders infinitely. It fetches data in useEffect but doesn’t have a dependency array. How do I fix it without breaking the data fetch?” |
Context = better answers.
Mistake 3: Accepting the First Output
AI outputs are drafts, not final answers. The best results come from iteration:
- First prompt: Get initial output
- Second prompt: “This is good but make it more concise”
- Third prompt: “Change the tone to be more technical”
- Fourth prompt: “Add code examples”
Each iteration refines the output. Don’t settle for the first attempt.
Advanced Techniques That Work
Chain-of-Thought Prompting
For complex reasoning tasks, ask the AI to think step-by-step:
“Solve this problem step by step. Show your reasoning before giving the final answer.
Problem: Our API response time increased from 200ms to 800ms after we added caching. Why might caching make things slower?”
The AI will reason through possibilities (cache lookup overhead, serialization cost, network latency to cache server) instead of jumping to conclusions.
Role Prompting
Different roles produce different outputs:
- “You are a helpful assistant” → Generic, safe answers
- “You are a senior engineer reviewing code” → Critical, detailed feedback
- “You are a teacher explaining to beginners” → Simple, step-by-step explanations
- “You are a debugging expert” → Systematic problem analysis
Match the role to your need.
The “Perspective” Trick
Ask for multiple perspectives:
“Analyze this architectural decision from three perspectives:
- A performance engineer worried about scalability
- A security engineer worried about vulnerabilities
- A developer worried about maintenance burden
What would each person say?”
This surfaces issues you might not have considered.
Model-Specific Tips
GPT-4.5 Turbo
- Excels at creative tasks and brainstorming
- Sometimes too verbose—add “Be concise” to prompts
- Great at following complex multi-step instructions
Claude 3.7
- Best for code review and refactoring
- Responds well to “Explain your reasoning” prompts
- Can handle very long context—paste entire files
Gemini 2.5
- Fast and cost-effective
- Great for summarization tasks
- Sometimes needs more explicit instructions than others
The Temperature Setting Nobody Talks About
Temperature controls randomness (0 = deterministic, 1 = creative):
| Task Type | Recommended Temperature |
|---|---|
| Code generation | 0.2 – 0.3 |
| Data extraction | 0.0 – 0.1 |
| Creative writing | 0.7 – 0.9 |
| Brainstorming | 0.8 – 1.0 |
| Summarization | 0.3 – 0.5 |
| Classification | 0.0 – 0.2 |
Most people leave it at default (0.7) for everything. That’s wrong. Match temperature to task.
Testing Your Prompts
Don’t guess if a prompt works—test it:
- Create 5 test cases (edge cases, typical cases)
- Run your prompt on all 5
- Grade the outputs (1-10 scale)
- Iterate on prompts that score below 8
Example: Testing a code review prompt
- Test 1: Clean, good code → Should say “looks good”
- Test 2: Code with security flaw → Should catch it
- Test 3: Code with performance issue → Should suggest optimization
- Test 4: Code with style issues → Should mention it
- Test 5: Perfect code → Should not over-criticize
If your prompt fails any test, refine it.
Real-World Prompt Templates
For Code Review:
– Bugs or logic errors
– Security vulnerabilities
– Performance issues
– Code style and readability
Be specific about problems and suggest fixes.
Rate severity as: CRITICAL, HIGH, MEDIUM, LOW
Code:
[paste code here]
For Documentation:
Include:
– Brief description (1 sentence)
– Parameters (name, type, description)
– Return value (type, description)
– Example usage (code snippet)
– Common errors and how to handle them
Function:
[paste function here]
For Debugging:
Expected behavior: [describe]
Actual behavior: [describe]
Error message: [paste if any]
Code: [paste relevant code]
Analyze systematically:
1. What could cause this?
2. How to verify the root cause?
3. How to fix it?
The Future: Prompt Engineering is Evolving
In 2025, we’re seeing:
- Automatic prompt optimization: Tools like DSPy automatically improve prompts
- Multimodal prompts: Combine text, images, and code in one prompt
- Prompt chaining: Breaking complex tasks into sequences of prompts
- Retrieval-augmented prompting: Automatically fetching relevant context
But the fundamentals remain: be specific, provide context, iterate on outputs.
Your Action Plan
- This week: Pick your most common AI task. Rewrite the prompt using the 5-part structure above.
- This month: Create a prompt library—save your best prompts for reuse.
- This quarter: Test and refine prompts. Track which ones consistently give great results.
Good prompt engineering is the difference between AI that occasionally helps and AI that genuinely multiplies your productivity.
Master it, and you’ll be 10x more effective than developers who treat AI like a magic 8-ball.