What is Genie AI and Why It Matters
What is Genie AI and Why It Matters
Genie AI is a conversational BI tool that lets you interact with your data using natural language. Instead of navigating complex dashboards or requesting custom reports, you can simply ask questions in a chatbot-style interface and get instant answers.
Creating a Genie space is like setting up your own data chatbot. It’s perfect for:
- Users who prefer natural language queries over traditional BI tools.
- Exploring data beyond what’s available in existing dashboards.
- Handling ad hoc questions without waiting for report updates.
Connecting Data and Applications with Genie
Genie isn’t just for humans; it works with your tools too. The Genie API allows apps, agents, and workflows to submit prompts and receive responses programmatically. Instead of exposing raw data and building comprehension into every tool, Genie acts as the smart layer between your data and your applications.
Additionally, when you configure AI/BI dashboards in Databricks, Genie automatically creates an associated space. This means:
- Users can ask follow-up questions while exploring dashboards.
- No need for exports or tweaks; just query Genie for deeper breakdowns.
- If insights prove valuable, data teams can incorporate them into permanent solutions.
Genie AI transforms BI from static to conversational, making data exploration faster, easier, and more intuitive. Whether you’re a business user, a developer, or a data team, Genie helps you unlock insights without extra effort.
How Genie AI Works
There are three main ways to interact with Genie within Databricks. In this post, I’ll focus on creating a Genie space for a specific business domain and configuring it. There’s extensive documentation on API interactions for those interested in deeper technical integration.
Imagine a Databricks platform that’s already set up, with data ingested through the medallion architecture resulting in gold-level data ready for enterprise reporting and BI. Genie builds on this foundation to enable conversational analytics and flexible exploration.
There will be questions coming from people’s use of tools or from the business that go beyond what’s in fixed reports. We can point a Genie space at that data, allowing users to go into the interface and ask natural language questions like:
- “What was my revenue last year?”
- “What was our average time to complete a project?”
Understanding Genie Spaces
That question is directed to a Genie space. When configured, the Genie space contains information about data within Databricks, governed by Unity Catalog. All tables and views we point Genie at have their metadata visible. Genie sees what kind of data is in the tables, and if we add comments or tags to columns and tables, those are also visible. This metadata helps Genie interpret the tables as well as any additional instructions or prompts.
Genie is not actively exploring the data itself. It is really a code tool, that uses all the metadata and associated guidance (see below) to interpret the user’s question, create a SQL query that can be run on the data to answer it, and then providing the result of the query back to the user.
Providing Guidance to Genie
We can provide Genie with various instructions to aid the understanding of the business context and the data structure. This can take the form of;
- General ‘prompt’ instructions to provide guidance on interpreting the user questions, understanding organisation-specific language, and what actions to take or not take in certain scenarios
- Table join instructions to enforce particular relationships (these are prepopulated if foreign key constraints exist between tables)
- Sample SQL queries, for when specific complex questions need to be answered in a certain way
- SQL expressions, to calculate specific metrics inside a query the system has generated
All guidance is included in the Genie configuration and stored in the Genie knowledge base. When a user asks a question, Genie gathers that information and sends it to Azure OpenAI, without sending any actual data.
Security, Governance, and Feedback
This is one of Genie’s biggest benefits: it doesn’t give an LLM (Large Language Model) all your business data and ask for an answer. Instead, Genie sends OpenAI the user’s question and context, then asks for a SQL query that can run against your Databricks tables. This approach is:
- More secure because no data leaves your environment.
- More accurate and consistent.
- Fully auditable, you can see exactly what query was run, when, and replicate the result.
With open LLMs, answers might come from the internet, which can change or be unreliable. Genie only uses the data you’ve provisioned, so you know where the answer came from. Queries respect Unity Catalog permissions and run as the logged-in user, so low-level security and governance rules apply. For example, if someone only has access to revenue data for certain regions or HR data for their direct reports, Genie enforces those rules.
Feedback is built in. Users can review Genie’s response, see the SQL code and logical steps, and mark whether it’s correct or submit it for review. This adds trust and control, and ensures that the solution can evolve with user requirements to maintain accuracy over time. All the feedback and reviews can be explored through the user interface and developers/reviewers can immediately take steps to address issues.
Future Developments
Genie spaces are still a relatively new feature to Databricks but it continues to evolve. Up next is Genie Research Agent which is currently in Beta at time of publishing. This takes Genie from basic data exploration to deep analysis, allowing users to ask more detailed ‘why’ questions, and have Genie plan a research methodology and execute it to come up with the answer. This is a further step forward in leveraging AI to enable better use of data beyond static reporting and visualisation.
Databricks Free Edition is now available, and comes with some common datasets built in, making it a great way to explore these features!