Token
In AI, a token is the basic unit of text that language models process — roughly equivalent to 4 characters or ¾ of an average English word. Tokens are used to measure context window capacity and determine API usage costs.
Understanding Token
Language models do not process text character-by-character or word-by-word. Instead, they operate on tokens — sub-word units produced by a tokenizer that breaks text into chunks based on frequency patterns in the training corpus. Common short words like 'the' or 'is' are typically single tokens, while longer or rare words may be split into two or more tokens. Understanding tokens is essential for two reasons. First, every model has a context window measured in tokens — the maximum amount of text it can consider at once. GPT-4o has a 128,000-token context window; Claude 3.5 Sonnet supports 200,000. Second, most LLM APIs charge per token consumed (input + output), so token awareness directly impacts cost. As a rough rule: 1,000 tokens ≈ 750 words, or about 1,500 characters. A typical business email is 200–400 tokens. A long research paper may exceed 8,000 tokens. When building AI applications, prompt design often involves carefully managing token usage to maximize context efficiency while controlling costs.
How GAIA Uses Token
GAIA manages token usage efficiently across all its language model calls to balance capability with cost. When processing long documents like email threads or meeting transcripts, GAIA uses chunking and summarization strategies to stay within model context windows. It selects the appropriate model tier — from lightweight models for simple tasks to frontier models for complex reasoning — partly based on the token budget required for each operation.
Related Concepts
Large Language Model (LLM)
A Large Language Model (LLM) is a deep learning model trained on massive text datasets that can understand, generate, and reason about human language across a wide range of tasks.
Context Window
The context window is the maximum number of tokens a language model can process in a single inference call, encompassing the system prompt, conversation history, retrieved documents, and generated output.
Prompt Engineering
Prompt engineering is the practice of designing and refining inputs to AI language models to reliably elicit desired outputs, shaping model behavior without modifying the underlying weights.
Hallucination
AI hallucination is the phenomenon where a language model generates confident-sounding but factually incorrect, fabricated, or nonsensical information that is not grounded in the input or training data.
Fine-Tuning
Fine-tuning is the process of taking a pre-trained AI model and continuing its training on a smaller, task-specific dataset to adapt its behavior for a particular domain or application.
