Term
Token Efficiency
Definition
Token efficiency is about how effectively an Artificial Intelligence (AI) model uses a given amount of text data, known as the context window, to create relevant and useful responses. It measures the quality of the output based on the input it processes.
Where you’ll find it
In AI systems, token efficiency is usually evaluated in the development and testing phases, particularly when fine-tuning language models. This concept appears across various AI platforms, where developers adjust model settings to optimize performance.
Common use cases
- Enhance the accuracy of language models in generating text that is contextually relevant.
- Improve the efficiency of chatbots in understanding and responding to user queries.
- Reduce the computational resources needed by ensuring that the model does not process more data than necessary.
Things to watch out for
- Token efficiency can vary greatly depending on the type, size, and training of the AI model.
- A context window that is too small might not provide enough data for accurate outputs, while an excessively large one may lead to wasted computational resources.
- Understanding the balance between window size and model capacity is key to optimizing token efficiency.
Related terms
- Context Window
- Model Hyperparameters
- Attention Mechanisms
- Language Models
- Computational Resources