AI and Honeydew

An AI or a large language model (LLM) can be capable of translating business questions like “What is our monthly profit in Seattle?” into data and insights for business users.

As a semantic layer, Honeydew provides AI with the context of your data model.

Using semantics as context, users can escape common pitfalls of applying LLMs to data - such as inconsistent or inaccurate results.

The core advantages of combining AI and a semantic layer:

1. Consistency: Honeydew provides AI with well-defined metrics and business entities. By leveraging a definition managed by Honeydew, AI can’t hallucinate a metric definition or create wrong SQL.
2. Time to value: Honeydew can operate on top of a complex data schema, with multiple fact tables and snowflake schemas. By providing an abstraction that AI understands, Honeydew enables to run AI directly on your data without transformations.
3. Complex Metrics: Many business metrics can be complex, with derivative calculations, time intelligence, dynamic levels of details, and multi-step calculations. As Honeydew manages the calculation of the metric, AI can use those metrics without making mistakes.
4. Combine BI and AI: Honeydew provides BI tools with the same semantics as it provides AI. That allows to maintain consistency of results between any BI dashboards and an AI chatbot response.

Honeydew can integrate with your own LLM and prompt (such as Mistral, Antropic Claude, Meta LLama or OpenAI GPT-4o), or with Snowflake Cortex.