18. Prompt Optimization

flowchart TD
    subgraph BEFORE["Before Optimization"]
        B1["2,000 token prompt"] --> B2["3 seconds per call"]
        B2 --> B3["$30/day for 10K calls"]
    end

    subgraph AFTER["After Optimization"]
        A1["500 token prompt"] --> A2["1 second per call"]
        A2 --> A3["$7.50/day for 10K calls"]
    end

    style BEFORE fill:#ef4444,color:#fff
    style AFTER fill:#22c55e,color:#fff

flowchart TD
    OPT["Prompt Optimization"] --> TOKEN["Token Optimization\nFewer tokens, same quality"]
    OPT --> LATENCY["Latency Optimization\nFaster responses"]
    OPT --> COST["Cost Optimization\nCheaper per call"]
    OPT --> QUALITY["Quality Optimization\nBetter outputs"]
    OPT --> RELIABILITY["Reliability Optimization\nMore consistent"]

    style OPT fill:#8b5cf6,color:#fff
    style TOKEN fill:#3b82f6,color:#fff
    style LATENCY fill:#22c55e,color:#fff
    style COST fill:#f59e0b,color:#fff
    style QUALITY fill:#ec4899,color:#fff
    style RELIABILITY fill:#14b8a6,color:#fff

❌ Before (150 tokens):
"I would like you to please act as a senior software engineer
who has extensive experience with the Python programming language
and specifically with the Django web framework. Could you please
take a look at the following code and provide me with your feedback
on any potential issues or improvements that you might notice?"

✅ After (50 tokens):
"Role: Senior Django engineer
Task: Review this code for issues and improvements
Code: [code]"

flowchart LR
    LATENCY["Latency Factors"] --> TOKENS_IN["Input Tokens\nMore input = slower"]
    LATENCY --> TOKENS_OUT["Output Tokens\nMore output = slower"]
    LATENCY --> MODEL["Model Size\nLarger model = slower"]
    LATENCY --> TEMPERATURE["Temperature\nHigher = more random = slower?"]

    style LATENCY fill:#f59e0b,color:#fff

Cost per call = (Input Tokens × Input Price) + (Output Tokens × Output Price)

Example (GPT-4o):
Input: 500 tokens × $2.50/1M = $0.00125
Output: 200 tokens × $10.00/1M = $0.00200
Total: $0.00325 per call

At 10,000 calls/day: $32.50/day = ~$975/month

flowchart TD
    QUALITY["Quality Optimization"] --> TEST["A/B Testing\nCompare prompt variants"]
    QUALITY --> FEEDBACK["User Feedback\nIncorporate real results"]
    QUALITY --> ITERATE["Iterative Refinement\nStep-by-step improvements"]
    QUALITY --> EVAL["Automated Evaluation\nMeasure quality metrics"]

    style QUALITY fill:#22c55e,color:#fff

flowchart LR
    MEASURE["1. Measure\nCurrent performance"] --> IDENTIFY["2. Identify\nBottlenecks"]
    IDENTIFY --> OPTIMIZE["3. Optimize\nApply techniques"]
    OPTIMIZE --> TEST["4. Test\nVerify quality maintained"]
    TEST --> MEASURE
    TEST --> DEPLOY["5. Deploy\nTo production"]
    DEPLOY --> MONITOR["6. Monitor\nTrack metrics"]

    style MEASURE fill:#3b82f6,color:#fff
    style IDENTIFY fill:#f59e0b,color:#fff
    style OPTIMIZE fill:#22c55e,color:#fff
    style TEST fill:#8b5cf6,color:#fff
    style DEPLOY fill:#ef4444,color:#fff
    style MONITOR fill:#ec4899,color:#fff

Before (1,200 tokens):
"As an AI assistant with expertise in customer support for e-commerce
platforms, I need you to analyze the following customer inquiry and
determine the most appropriate response based on our company policies
and procedures. Please consider the customer's tone, the nature of
their issue, their order history if available, and our current return
and refund policies..."

After (400 tokens):
"System: E-commerce support agent
Follow policy guidelines below for each issue type.

[Customer inquiry]
[policies]

Respond with: resolution, policy_ref, customer_tone, needs_escalation"
→ 67% token reduction, same quality

Before: "Please return the data in a JSON format with the following fields..."
After: "Return JSON: { field1: type, field2: type }"
→ 50% token reduction, more consistent outputs