Maximizing Token Efficiency

This document provides guidance on how to maximize token efficiency when using CodeAI.

Understanding Token Usage

Tokens are the basic units of text that large language models process. Efficient token usage is crucial for managing costs and improving performance.

Strategies for Token Efficiency

• Concise Prompts: Write clear and direct prompts, avoiding unnecessary words or phrases.
• Context Management: Only provide relevant context to the model. Remove redundant or outdated information.
• Summarization: If dealing with long texts, consider summarizing them before feeding them to the model.
• Batching: For multiple small requests, consider batching them if your model supports it to reduce overhead.
• Model Choice: Different models have different token limits and cost structures. Choose a model that fits your needs.

Tools and Techniques

• Tokenizers: Use tokenizers to estimate token counts before sending requests.
• Prompt Engineering: Experiment with different prompt structures to find the most token-efficient way to achieve your desired output.
• Caching: Cache responses for frequently asked questions or common queries to avoid re-generating content.