Prompt Optimization
Techniques for optimizing prompts to improve AI response quality, reduce token usage, and achieve consistent results across models.
Prompt Optimization
Prompt optimization is the process of refining your prompts to get better results with fewer tokens. Well-optimized prompts produce more consistent outputs, reduce costs, and improve reliability across different AI models.
Optimization Principles
1. Be Specific, Not Verbose
❌ Bad: "Can you please help me by writing some code that does something
related to sorting maybe in Python if that's okay?"
✅ Good: "Write a Python function that sorts a list of dictionaries
by a specified key."
2. Remove Redundancy
❌ Bad: "I want you to write. Please write a summary. Write a summary
of the following text. Here is the text to summarize..."
✅ Good: "Summarize the following in 3 bullet points:"
3. Use Structured Formats
❌ Bad: "Tell me about the person including their name and age and job..."
✅ Good: "Extract from the text:
- Name:
- Age:
- Occupation:"
Techniques
Few-Shot Optimization
Provide 2-3 examples that cover edge cases:
Example 1: Input → Expected Output
Example 2: Input → Expected Output
Example 3: Edge case → Expected Output
Now process: {actual_input}
Role Prompting
You are a {specific role} with {relevant expertise}.
Your task: {clear objective}
Constraints: {limitations}
Output format: {expected structure}
Constraint Specification
Rules:
- Maximum 100 words
- Use bullet points
- No jargon
- Include one example
- End with actionable advice
Output Templates
Respond in this exact format:
## Summary
[2-3 sentences]
## Key Points
- [point 1]
- [point 2]
- [point 3]
## Action Items
1. [action]
2. [action]
Token Optimization
| Technique | Token Savings | Quality Impact |
|---|---|---|
| Remove filler words | 10-20% | None |
| Use abbreviations in instructions | 5-10% | Minimal |
| Concise examples | 15-25% | None if clear |
| Structured formats | 20-30% | Often improves |
Testing Optimizations
- A/B test prompts - Run same input through old and new prompts
- Measure consistency - Same input, multiple runs, check variance
- Check edge cases - Test with unusual or boundary inputs
- Monitor token counts - Track cost alongside quality
Common Anti-Patterns
- Over-prompting - Adding unnecessary constraints that confuse the model
- Example overload - Too many examples that dilute the pattern
- Contradictory instructions - Rules that conflict with each other
- Implicit assumptions - Requirements the model can't know
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