Token Efficiency & Context Management
Learn to manage token usage effectively to reduce API costs and optimize the context window for better LLM performance.
Understanding LLM Tokens
When working with Large Language Models (LLMs), a fundamental concept is the token. Tokens are the basic units of text that an LLM processes. They can be whole words, parts of words, or even punctuation marks.
LLM APIs, like those from OpenAI or Anthropic, typically charge you based on the total number of tokens used for both your input (the prompt) and the model's output (the response). Efficient token management directly impacts your operational costs.
The Context Window Explained
Every LLM has a limited context window. This is the maximum number of tokens it can 'see' and process at any given time. Think of it as the LLM's short-term memory.
The context window includes everything: your instructions, any provided context, the user's input, and even the LLM's own generated response. Exceeding this limit will result in an error, as the model cannot process more information.
All lessons in this course
- Token Efficiency & Context Management
- Latency Reduction Techniques
- Output Parsing & Validation
- Caching and Batching for LLM Cost Savings