> For the complete documentation index, see [llms.txt](https://docs.alkemi.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.alkemi.ai/documentation/basics/data-products/training-your-data-product/best-practices.md).

# Best Practices

### Building Effective Training Data

1. **Start with common queries**: Focus on the questions users ask most frequently
2. **Include edge cases**: Add examples for complex or unusual queries
3. **Maintain diversity**: Cover different aspects of your data schema
4. **Regular updates**: Add new examples as user needs evolve

### Quality Over Quantity

* 20 high-quality examples are more valuable than 200 poor ones
* Focus on accuracy and relevance to your specific use cases
* Regularly review and refine existing training data

### Monitoring and Improvement

1. **Track Agent performance**: Note when the Agent struggles with certain types of queries
2. **Add missing examples**: Create training data for queries the Agent couldn't handle
3. **Review certainty ratings**: Regularly check and fix low-certainty queries
4. **Iterate**: Training is an ongoing process—continuously improve your dataset

### Common Pitfalls to Avoid

* **Ambiguous prompts**: Ensure prompts clearly indicate the desired outcome
* **Outdated examples**: Remove training data that references deprecated tables or columns
* **Duplicate concepts**: Avoid too many similar examples that don't add value