Memorang changelog
EdFlow: Enhancing Agent Training with Curated Datasets
High-quality training data is essential for developing effective AI agents. EdFlow now provides Datasets, allowing you to curate collections of data specifically for agent training. By tagging examples as "good" or "bad," you help the agent learn from both positive and negative instances.
For instance, when reviewing agent outputs, you might find responses that meet your criteria. Tagging these as "good" includes them in the training data as positive examples. If an output doesn't meet your standards, tagging it as "bad" adds it as a negative example, teaching the agent to avoid similar mistakes in the future.
By building datasets with both positive and negative examples, you can improve your agents' performance based on actual interactions and your specific guidelines.โโโโโ๏ปฟโ๏ปฟโโโโโโ๏ปฟ๏ปฟโ๏ปฟโโโโโโโโโ๏ปฟโโโโโโ๏ปฟโโโโโโ๏ปฟโโโโโโโ๏ปฟโ๏ปฟโโโโโโ๏ปฟโโโโโโ๏ปฟโโโ๏ปฟโโโโ๏ปฟโโโโโโโ๏ปฟ๏ปฟโโโโโโ๏ปฟโโโโโโโโโโ๏ปฟโโโโโโโโโโโโโโโ๏ปฟโโโโโโโโ๏ปฟ๏ปฟโ๏ปฟโ๏ปฟโ๏ปฟโโโ๏ปฟโโโโโโโโโโโโ๏ปฟ๏ปฟโโ๏ปฟ๏ปฟโโโโโโ๏ปฟโโ๏ปฟโโโโโโโโ๏ปฟโโ๏ปฟโโโโ๏ปฟ๏ปฟโโโโโโโโโโโโโ๏ปฟโโโโ๏ปฟ๏ปฟโโ๏ปฟโโโ๏ปฟ๏ปฟโโโโโโโโโ๏ปฟ๏ปฟโโ๏ปฟโโโ๏ปฟโโโโโโโ๏ปฟโ๏ปฟโโโโโโ๏ปฟโโ๏ปฟโโโโโโโ๏ปฟโโโโโโโโ๏ปฟ๏ปฟโโ๏ปฟโโ๏ปฟโ๏ปฟโโโโโโโโโ๏ปฟ๏ปฟโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโโโโโโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโ๏ปฟโโ๏ปฟโ๏ปฟโ๏ปฟโ๏ปฟโโโโโโโโโโ๏ปฟโโ๏ปฟโโโโโโโโโโโโโโโโ๏ปฟโโโโโโ๏ปฟโโโโโโโ๏ปฟโ๏ปฟโโ๏ปฟโโโโโโ๏ปฟโโโโโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโโโโโโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโ๏ปฟโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโโโโ๏ปฟ๏ปฟโโโโ๏ปฟโโโโโโโโโโ๏ปฟโโโโ๏ปฟโโโโโโ๏ปฟโโโ๏ปฟ๏ปฟโโโ๏ปฟโโโโโ๏ปฟโโโโโโโโ๏ปฟโโ๏ปฟโ๏ปฟโ๏ปฟโโโโโโโ๏ปฟโโโโโโโ๏ปฟ๏ปฟโโโ๏ปฟโโโ๏ปฟ๏ปฟโโโ๏ปฟโโโโโ๏ปฟโโโโโโโโ๏ปฟ๏ปฟโโโ๏ปฟโโโโโ๏ปฟโโโโโโโ๏ปฟโ๏ปฟโโโโโโโโโ๏ปฟโ๏ปฟโโโโโโโโโ๏ปฟโโโ๏ปฟโโโโโโโ๏ปฟโโโโโโโ๏ปฟโโโ๏ปฟโ๏ปฟโโโโโ๏ปฟโโโ๏ปฟโโโโโโโโโโโโโโโ๏ปฟโโโโโโโ๏ปฟโโโ๏ปฟโโโโโโโ๏ปฟโโโโโโโโ๏ปฟโโโ๏ปฟโโโโโโ๏ปฟโโโ๏ปฟ๏ปฟ๏ปฟโโโโโโโโโ๏ปฟโ๏ปฟโโโโโโโโโ๏ปฟโโโโ๏ปฟโโ๏ปฟ๏ปฟโโโโโโ๏ปฟโโโโโโโ๏ปฟโ๏ปฟโ๏ปฟโโโ๏ปฟโโโโโโโโโโโโ๏ปฟ๏ปฟโโโโโโโโโโโโโ๏ปฟ๏ปฟโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโโโโโโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโ๏ปฟโโ๏ปฟโ๏ปฟโ๏ปฟโ๏ปฟโโโโโโโโโโ๏ปฟโโ๏ปฟโโโโโโโโโโโโโโโโ๏ปฟโโโโโโ๏ปฟโโโโโโโ๏ปฟโ๏ปฟโโ๏ปฟโโโโโโ๏ปฟโโโโโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโโโโโโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโโโโ๏ปฟโโโ๏ปฟโโโ๏ปฟโโโโโโโ๏ปฟ๏ปฟโโโโ๏ปฟโโโโโโโโโโ๏ปฟโโโโ๏ปฟโโโโโโ๏ปฟโโโ๏ปฟ๏ปฟโโโ๏ปฟโโโโโ๏ปฟโโโโโโโโ๏ปฟโโโโโโ๏ปฟโโโโโโโ๏ปฟโโโโโโโ๏ปฟ๏ปฟโโโ๏ปฟโโโ๏ปฟ๏ปฟโโโ๏ปฟโโโโโ๏ปฟโโโโโโโโ๏ปฟ๏ปฟโโโ๏ปฟโโโโโ๏ปฟโโโโโโโ๏ปฟโ๏ปฟโโโโโโโโโ๏ปฟโ๏ปฟโโโโโโโโโ๏ปฟโโโ๏ปฟโโโโโโโ๏ปฟโโโโโโโ๏ปฟโโโ๏ปฟโ๏ปฟโโโโโ๏ปฟโโโ๏ปฟโโโโโโโโโโโโโโโ๏ปฟโโโโโโโ๏ปฟโโโ๏ปฟโโโโโโโ๏ปฟโโโโโโโโ๏ปฟโโโ๏ปฟโโโโโโ๏ปฟโโโโโโโ๏ปฟ๏ปฟโ