Using Databricks Genie to Understand User Behavior and Drive AI Innovation

5 min read1 day ago

In the world of AI, businesses often jump on the Generative AI (Gen AI) bandwagon, sometimes without clear use cases, driven by the fear of missing out. Over the past few months, I’ve spent time understanding Databricks Genie to determine how it can be leveraged, especially in environments where the business teams and end-users lack clarity on what they need from AI.

But first, let me explain what Databricks Genie is. At its core, Genie is an agentic system — a simple yet powerful tool that answers questions from structured data. Genie has multiple tools like trusted assets, which are predefined functions and example queries that deliver verified answers to anticipated questions. These tools try to make Genie as reliable as it can, ensuring that when it invokes a trusted asset, the response is validated for accuracy.

To process responses, Genie uses the following:

The natural language prompt submitted by the user
Table names and descriptions
Column titles and descriptions
General instructions
Example SQL queries
SQL functions

Below is the flow in a typical agentic architecture

A user submits a question via a web UI, which includes both state (chat history/memory) and the user query.

The agent (which is essentially a large language model, or LLM) determines the optimal approach to solve the problem using only the tools necessary for that specific query.
Agents abstract the complexity of multi-step problem-solving, activating only the tools that will lead to a solution. This seamless architecture enables it to integrate various tools and data sources, evolving with new tasks and queries (tool 1, tool 2… tool n)

How to use Genie to your benifit

Leveraging Databricks Genie doesn’t require an overly complex or prescriptive approach. In fact, the key to maximizing its value lies in adopting a lean experimentation mindset and observing user behavior. By continuously iterating and refining your AI strategy based on real-time insights, you can uncover hidden opportunities and shape the future of your AI implementation.

Here’s how you can effectively use Genie to derive value for your organization:

1. Implement as a Learning Tool for User Behavior

Rather than assuming you know what your users need, let Genie do the work of revealing those patterns. By rolling it out to a select group of users, you’ll start to gain valuable insights into how they interact with the tool and what queries they’re looking to have answered.

Track Frequent Queries: If certain questions are asked repeatedly, this is an indication that users are looking for a standard or consolidated answer. It may be a good idea to automate these answers further or build a dashboard to visualize answers, freeing up users’ time and enhancing their experience.
Identify Dissatisfaction: If users are frequently asking specialized or complex questions that Genie is unable to answer satisfactorily, this could signal an unmet need. This frustration points to a new use case where a more tailored AI model or a machine learning solution might be the answer. Genie helps you identify pain points that you might not have been able to anticipate upfront.

By leveraging Genie as an active tool for uncovering user intent and pain points, you can design more targeted AI-driven solutions. This isn’t about setting Genie loose and walking away — it’s about building a feedback loop that guides the evolution of both the tool and your overall AI strategy.

2. Create Custom Dashboards for Repeated Queries

As you observe repeated user questions, you may notice that they fall into categories. For instance, users may continually ask for specific metrics or reports that could be consolidated into a single view.

This is where Genie can be used as a discovery tool for creating dashboards that automate the information users need most. Think of it as a way to improve data accessibility by:

Automating responses to common questions through self-service dashboards or reports.
Streamlining workflows by providing users with quick access to key data points or insights.

By shifting frequently asked questions into a predefined dashboard, users can independently access answers, reducing reliance on customer service or data engineering teams. Over time, this will help optimize the user experience while freeing up valuable resources within your organization.

3. Spot New Use Cases for AI

Sometimes, a disappointing response from Genie can be more insightful than a successful one. If users consistently ask complex, domain-specific questions and Genie can’t fully satisfy their needs, it opens up an opportunity for a customized solution.

This is where you can start thinking about next-level AI strategies, whether through:

Classical Machine Learning Models: Perhaps there’s a need for predictive analytics, anomaly detection, or a recommendation engine that Genie can’t fulfill through basic query-answering.
Advanced Gen AI Models: If users are requesting very complex or creative outputs — like generating natural language insights from data or coming up with unique solutions — this could indicate that Genie’s current capabilities fall short. However, this can lead to implementing more advanced AI models, such as Generative AI, to meet these needs.

Understanding that Genie may have gaps in solving certain queries doesn’t mean it’s a failure; it just signals that you’re at the forefront of identifying where to expand the use of AI within your organization.

4. Leverage Genie for Continuous Feedback Loops

AI systems, like Genie, thrive in an environment of constant iteration and feedback. After users interact with Genie, you should establish mechanisms to collect feedback on the quality of answers, the relevance of the response, and overall user satisfaction.

By creating a feedback loop, you ensure that the AI continues to evolve. For instance, you could:

Monitor which responses users find helpful and which they don’t.
Tweak Genie’s database or models to improve the output.
Use feedback to refine the types of questions that Genie should prioritize in future releases.

The more responsive your AI tool is to real-time feedback, the more effective and aligned it will become to user expectations. This iterative feedback model creates an evolving system that doesn’t just respond to the present but adapts for the future.

5. Foster Collaboration Between AI and Human Expertise

As Genie becomes more adept at answering questions and solving problems, don’t forget to maintain a collaborative relationship between Genie and human expertise. While AI can reduce repetitive tasks, it cannot replace critical thinking, creativity, or nuanced decision-making that humans provide. By positioning Genie as a partner rather than a replacement, your organization can enjoy the best of both worlds: AI-powered efficiency and human-driven insight.

You can further improve collaboration by:

Using Genie to automate repetitive tasks, allowing your data analysts or subject matter experts to focus on strategic analysis, insights, and decision-making.
Encouraging employees to interact with Genie to validate hypotheses or support their decision-making process, integrating AI into human-driven workflows for maximum impact.

Final Thoughts

Databricks Genie isn’t just a tool for answering questions; it’s a launchpad for discovering deeper insights about your users, improving workflow efficiency, and identifying new areas where AI can add value. By treating it as an evolving system that benefits from user feedback, real-time monitoring, and integration with human expertise, Genie can unlock new capabilities and help your organization stay ahead in the fast-moving world of AI.