Term
Token Limit
Definition
The Token Limit refers to the highest number of tokens (pieces of data) that an AI model can handle in one go, either when receiving information (input) or delivering results (output).
Where you’ll find it
This limit is typically noted in the AI platform's documentation or within the settings area, particularly where models and processing options are configured.
Common use cases
- Training AI models to ensure they process data efficiently without overload.
- Optimizing AI interactions to balance performance and speed.
- Adjusting the complexity of inputs and outputs based on the model's capabilities.
Things to watch out for
- Exceeding the Token Limit can lead to errors, incomplete data processing, or unexpected model behavior.
- Not all AI platforms allow for the Token Limit to be adjusted, which might limit the complexity of the tasks you can run.
- Always check the Token Limit when using pre-trained models or switching between different AI platforms to ensure compatibility.
Related terms
- Model Training
- Data Tokenization
- Input/Output Capacity
- Processing Efficiency
Pixelhaze Tip: Always check the Token Limit before running complex queries. Splitting data into smaller chunks can help manage processing within the token constraints, aiding in avoiding errors and ensuring your model runs smoothly.