Getting Gemini and Looker to communicate end-to-end is trivial. The accuracy of the experience is unlocked through a robust semantic layer, the grounding of the language model and prompt training to tune Gemini to accurately translate the question and select the right objects in LookML. Creating data agents with Gemini is very similar to creating curated self-service analytics – we focus on the end user, the questions they need to be able to answer and then curate the experience.
Demystifying the training process
Training sounds complicated, but it’s really similar to a traditional analytics problem. Just like when we build a dashboard, we identify the business questions our end users need to be able to answer and then build a set of visualizations to help make decisions.
In the training process, we’re taking the same approach. We come up with a list of business questions and provide Gemini the expected Looker Explore result. By providing Gemini common business questions and the expected results, you will see dramatic improvements in the accuracy of the auto-generated responses.
Lowering barriers grows data-empowered users
The delivery of trusted AI in Business Intelligence (BI) is a significant development for internal and productized analytics. We are breaking down even more barriers to adoption as we simplify the interface for users to answer data questions. Other customers are leveraging AI in BI to deliver analytics to their front line workers who just need a quick answer.
We’re excited to partner with Google Data Cloud to demystify the process and deliver accurate, governed AI-powered BI. Reach out to Bytecode if you are searching for an experienced partner to help realize your analytical AI agent use cases, or visit the Google Cloud console to access Looker, Vertex AI and Gemini models to get started on your own.
