Training your Data Product

This guide covers how to create, manage, and optimize your training data through the Text to SQL Training interface.

Our AI Agent serves as your personal data scientist, proficient in interpreting natural language queries and converting them into SQL to analyze your connected data sources. The effectiveness of the Agent's responses relies heavily on high-quality examples of user prompts paired with their corresponding SQL queries.

Creating Training Data

The Text to SQL Training feature consists of two primary approaches:

Creating Manual Training DataCreating Automated Training Data

Both methods enhance the training dataset, improving the Alkemi Agent's capacity to understand and respond to questions about your specific data.

Reviewing and Managing Training Data

Quality control is crucial for ensuring an efficient Agent. The system supports various review workflows tailored to the data source and confidence levels.

Reviewing Pending QueriesReview Questionable Training Examples

Helpful Tips

Our Recommended Approach

1. Begin with manually crafting essential queries.

2. Transition to automated generation with manual oversight.

3. Regularly assess queries that have low certainty.

4. Continuously monitor agent performance and refine training data.

5. Gradually increase automation as quality enhances.

Instruct: Quality training data enhances Agent learning. Investing time in this process leads to better performance and increased user satisfaction.

Best PracticesTroubleshooting