A dataset in Future AGI is a structured collection of data that serves as the foundation for executing LLM prompts, conducting experiments, and optimizing AI-generated responses.
It organizes data in rows and columns, where each row represents an instance, and columns define the attributes associated with that instance.
Datasets provides the necessary context, inputs, and evaluation references for prompt execution and iterative improvements.
The dataset system is designed to support a full lifecycle of data management, ensuring flexibility, scalability, and usability across different AI workflows.
Datasets can be enriched with additional metadata and evaluations, including:
Annotations : Users can manually add the labels for a dataset defining their own set of rules and labels. Future AGI also provides auto-annotations which learn from the human in the loop and helps annotating the remaining datapoints. Learn more →
Evaluations : Users can utilize Future AGI Evaluations to evaluate the datasets to filter out the specific noise etc