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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Prompt Optimization