In today’s fast-paced data landscape, business users want answers without waiting for a data team to write SQL queries. The Genie Space framework bridges this gap by allowing users to ask data questions in plain language and get back the results, the SQL logic, and a clear explanation. Powered by LLMs and guided by curated data environments, Genie Space is more than an innovative query tool — it’s a collaborative system designed to make analytics truly self-service.
What Is the Genie Space Framework?
The Genie Space framework is an intelligent data interaction system that empowers business users to query and explore datasets using natural, everyday language. It integrates large language models (LLMs) with enriched metadata, curated datasets, and domain-specific business logic configured by the data team. This orchestration enables non-technical users to generate precise and context-aware analytical insights, while a built-in feedback loop continuously refines the system’s performance and semantic accuracy over time.
Key Components of the Genie Space System
The system is built around three core roles:
- Business Users – Ask questions in natural language.
- Data Team – Prepares the Genie environment with relevant data, logic, and instructions.
- LLM Engine – Converts user questions into SQL and provides answers with context.
How It Works: Step-by-Step
The data team begins by creating a Genie Space tailored to a specific topic (e.g., pricing, promotions). This includes:
- Selecting trusted datasets
- Adding metadata like column descriptions and definitions
- Creating example SQL queries
- Defining business rules and instructions
- Business users ask questions like: “Which stores had stock below 20 units for item 12345 on April 1st?”
- Genie analyzes the question, builds the SQL query, and returns:
- The result
- The SQL query
- An explanation of how it got the answer
Feedback from users helps the data team refine:
- Metadata
- Sample queries
- Instructions and rules
- This cycle ensures that Genie becomes smarter and more accurate over time.
Why Data Quality Matters?
The Genie Space framework performs best when input data is well-prepared. If column names are unclear, data types are inconsistent, or business logic is missing, Genie’s responses can be off. As the saying goes:
“Garbage in, garbage out.”
To avoid this, datasets should be clean, annotated, and tested.
Best Practices for Building Effective Genie Spaces
Based on hands-on experience, here’s a checklist for building Genie Spaces that actually work:
Start with focused, high-quality datasets. Avoid large, noisy data that confuses the model.
Use clear table relationships (primary and foreign keys). This helps the Genie engine understand joins and logic better.
Create simplified views for users and set appropriate access privileges in Unity Catalog. Missing SELECT access can break the process.
Use AI tools or manual effort to annotate:
- Column meanings
- Business terms
- Example usage
- This boosts the model’s interpretive power.
Before launch, test thoroughly:
- Ask different phrasings of questions
- Check SQL output
- See if answers align with expectations
Create reusable query functions for standard calculations or metrics (e.g., “Convert EUR to USD for beer prices”).
Real examples guide the model in handling similar future questions. These act like templates for understanding.
5.8 Collect and Act on Feedback
Encourage users to rate answers. Analyze patterns and improve where necessary, especially for frequently asked questions.
Real Example: Supporting Store Inventory Queries
- Curate trusted datasets with comprehensive metadata.
Let’s say you are creating a Genie Space to support store-level inventory questions.
You might curate a dataset called store_inventory_snapshot with the following trusted structure and metadata.
- Include sample questions paired with expected SQL output.
For sample Question is: Which stores had less than 20 units of item SKU 12345 in stock on April 1st, 2024?”.
SELECT store_id, item_sku, on_hand_quantity, inventory_date FROM store_inventory_snapshot WHERE item_sku = ‘12345’ AND inventory_date = ‘2024–04–01’ AND on_hand_quantity < 20;
- Document business context, including terminology and metrics definitions.
Ex Dataset: product_pricing_snapshot
- Review and act on user feedback to improve the model’s responses.
- Monitor usage to identify recurring patterns and adjust accordingly.
- Avoid onboarding datasets without proper descriptions.
- Do not rely on ambiguous SQL examples.
- Refrain from hardcoding temporary logic that violates business rules.
- Do not overlook feedback — especially if patterns of confusion emerge.
How I Helped Build Genie Spaces for Business Users?
As someone who is still relatively new to the data world, I had the opportunity to work on something really exciting — it’s called the Genie Spaces Concept Map. Essentially, this tool allows business users to ask questions in plain English, and it provides answers along with the corresponding SQL queries and clear explanations.
Let me explain how I helped put it all together
The Problem We Were Solving
“What was our sales last quarter by store?”
“Can I get a list of all promotions that didn’t perform well?”
They didn’t know SQL — and honestly, not all of us enjoy writing the same queries over and over. So we needed a smarter way to let them get answers themselves — safely and accurately.
The System I Helped Build
We broke the solution into 3 parts — I was mainly involved in the data setup part.
1. Data Teamwork (What I Helped With)
- We created Genie Spaces — kind of like smart folders for specific data topics (like pricing, products, etc.)
- Added sample questions with real SQL answers
- Shared business context (like what “KVI” means, or how we define a “promotion”)
- Collected feedback from users and kept improving
2. Genie Space “Brain” (The AI Part)
This is where it gets cool. The system uses everything we give it — sample queries, metadata, business rules — to understand natural language questions.
When a user asks:
“How did Category X perform last month?”
It gives back:
— A SQL query
— The actual answer
— A short explanation of how it figured it out
3. What Business Users Do?
- Ask questions in normal words (no SQL!)
- Get answers + SQL behind it
- Give feedback if something’s off or unclear
We even added a Message Feed so we can see what’s being asked a lot, what errors come up, and where we can improve.
What I Learned!
- Why good metadata and sample queries make or break AI-based tools
- How important feedback loop?
And honestly? It felt great to see business users get excited because now they don’t need to wait for SQL help every time.
Another Example :
Optimizing the Genie Space for Enhanced Data Interaction
To make Genie Spaces more effective for self-service analytics, it’s important to design and prepare them thoughtfully. Below are the best practices I’ve learned while building and optimizing Genie Spaces:
1. Selecting the Right Dataset
Choosing the right dataset is foundational. It should be relevant to the business context, well-structured, and clearly defined. Avoid cluttered or overly broad datasets that could confuse the Genie engine.
2. Structuring Data with Constraints
Apply relational constraints like primary keys and foreign keys to define relationships between tables. These constraints enable Genie to join tables effectively and interpret queries more accurately.
3. Streamlining Data and Views
Create views that exclude unnecessary columns, keeping the data clean and coherent. This simplifies user queries and improves model performance.
Also, ensure that access privileges are granted appropriately — especially SELECT privileges — since Genie relies on Unity Catalog governance. Missing privileges will result in access errors.
4. Documenting and Annotating Data
Comprehensive documentation helps the Genie engine understand the data better. Use AI-generated documentation to save time while ensuring alignment with domain-specific needs. Include column descriptions, business definitions, and usage examples.
5. Preparing and Testing Genie Spaces
Testing is essential. Assess the Genie Space by:
- Rephrasing sample questions
- Reviewing SQL output quality
- Refining instructions
Translate business language into meaningful instructions so the model can interpret company-specific jargon and domain concepts.
6. Developing Trusted Assets
Build predefined queries that answer common questions and register them as functions.
Example: A function that converts beer prices from EUR to USD. These functions provide consistency and simplify repetitive logic for the Genie.
7. Providing SQL Examples
Give the Genie engine clear SQL examples for frequently asked questions. These examples act as blueprints for the LLM to pattern-match and generate accurate results.
8. Improving Genie Through Feedback
Encourage users to rate responses (thumbs up/down). This feedback loop identifies weak areas in query understanding or SQL generation. Use it to enhance instructions and refine the Genie Space over time.
9. Enhancing Semantic Modeling
Explore semantic modeling techniques to improve Genie’s comprehension.
Techniques like Entity Relationship Diagrams (ERDs) help visualize data relationships.
Also:
- Enhance column metadata (data types, descriptions)
- Provide dataset-level instructions
- Define domain scope for the Genie Space
- Offer aggregated examples for complex scenarios
Conclusion
Optimizing Genie Spaces is a multidimensional task that spans data engineering, documentation, AI modeling, and user experience. By embedding semantic clarity, governance, and feedback-driven iteration, organizations can significantly enhance the utility of Genie Spaces, reducing reliance on technical intermediaries and enabling true self-service analytics at scale.
More read at https://docs.databricks.com/gcp/en/genie/
The outlined practices not only improve AI interpretability but also foster trust, transparency, and sustained adoption among business users. Read another article by author at https://journals-times.com/2025/01/07/fetch-data-from-data-lake-microservices-data-architecture/
Just put your email below, it is free!
- All Articles
- All English Articles
- Entrepreneurs Stories
- Health Topics
- Hindi Stories
- Research & Journals
- Technology
