AI Prompt Engineering & LLM Token Cost Optimization Guide

# AI Prompt Engineering & LLM Token Cost Optimization Guide As Large Language Models (LLMs) like GPT-4o, Claude 3.5 Sonnet, and Llama 3 become integral to enterprise software architectures, optimizing prompt structure and token consumption is vital for cost control and latency reduction. ## Understanding Tokenization in LLMs LLMs do not process raw words; they process tokens (sequences of characters or sub-words). In English text, 1 token equals approximately 4 characters or 0.75 words. ### Best Practices for System Prompts 1. **Use Explicit XML Tags**: Enclose instructions, constraints, and context inside XML tags (`<identity>`, `<rules>`, `<context>`). 2. **Define Structured Outputs**: Use JSON Schema definitions for OpenAI Function Calling to enforce deterministic JSON API responses.