Test chunk

utput tokens is much higher than input tokens. A request that generates 2000 output tokens may incur output costs 3-5 times higher than the same amount of input tokens.

2.2 Real Composition of Token Consumption

# Token Structure of One RAG-Augmented Generation
token_breakdown = {
    "systemPrompt": 2500,
    "ragContext": 4000,
    "ragMetadata": 500,
    "historyMessages": 3000,
    "userQuery": 200,
    "agentReasoning": 1500,
    "toolCalls": 800,
    "finalResponse": 1200,
    "totalInput": 10200,
    "totalOutput": 3500,
}
# Cost Calculation (Claude Opus 4):
# Input:  10200 * $15 / 1e6  = $0.153
# Output: 3500 * $75 / 1e6   = $0.2625
# Total: $0.4155 per request

# DAU 1000 users x 5 reqs/day x $0.4155 x 30 days = $62,325/month

2.3 Hidden Costs: Tokens That Burn Money Easily Overlooked

• Repeated Context Transmission: System prompts and embedded documents re-transmitted every turn
• Chain-of-Thought Inflation: CoT reasoning token consumption often exceeds final response by 5-10x
• Tool Call JSON Overhead: Schema with 10 tool definitions may consume 2000+ tokens
• Structured Output Format Tokens: Additional formatting overhead for json_mode or tool_use
• Error Retries: Auto-retries on 429 rate limits and 500 errors cause double token consumption
• Agent Loop Redundancy: Repeatedly reading same files and computing same content in loops

3. Cost Optimization Lesson 1: Context Engineering

3.1 Prompt Cache Architecture Design

# Cache optimization: Move variable content to end of Prompt
def make_cached_request(client, system_messages, history_messages, user_query):
    system = [
        {
            "type": "text",
            "text": SYSTEM_INSTRUCTIONS,
            "cache_contr