How to Build a RAG Over Your Microsoft Fabric Data – The Most Simple and 100% Low-Code Approach!

Introduction

In today’s data-driven world, businesses need instant access to insights without the complexity of SQL queries or coding. Microsoft Fabric AI Skills makes this possible by enabling users to create Retrieval-Augmented Generation (RAG) systems over lakehouses, warehouses, Power BI models, and KQL databases—all with zero or minimal code! ??

With this feature, your team can ask questions in plain English, and Fabric will generate SQL, DAX, or KQL queries to return accurate, structured answers. The best part? It’s a 100% low-code approach, making it accessible to business users and data professionals alike.

What is Retrieval-Augmented Generation (RAG)?

RAG is a powerful AI technique that combines retrieval-based search with generative AI to deliver precise answers using structured and unstructured data. Instead of relying only on predefined knowledge, RAG retrieves relevant data from sources like Microsoft Fabric lakehouses and warehouses and enhances responses with AI-generated insights.

?? Step-by-Step Guide to Creating a RAG AI Skill in Microsoft Fabric

This guide will help you set up an AI-powered RAG solution in Microsoft Fabric—the easiest way possible! Let’s get started:

Step 1: Create an AI Skill in Microsoft Fabric

1?? Navigate to your Fabric workspace.

2?? Click “+ New Item” > Search for AI Skill.

3?? Select AI Skill and provide a descriptive name (e.g., “Sales Insights AI”).

4?? Click Create to initialize your AI skill.

?? Step 2: Connect Data Sources

1?? Add up to five data sources such as:

• Lakehouses (structured & unstructured data)
• Warehouses (SQL-based structured data)
• Power BI Semantic Models (for business reporting)
• KQL Databases (for real-time analytics) 2?? The OneLake Catalog will appear, allowing you to select and add data sources. 3?? Ensure you have read/write permissions for Power BI semantic models.

?? Step 3: Enable AI Queries Over Data

Once data is connected, you can ask questions directly within the AI Skill interface. Examples: ? “What were our total sales in California in 2023?” ? “Which products had the highest revenue growth last quarter?” ? “What are the top-performing regions for our marketing campaigns?”

The AI Skill automatically generates SQL, DAX, or KQL queries to retrieve the correct answer from your dataset! ??

?? Step 4: Optimize AI Skill with Custom Instructions

To improve accuracy, you can fine-tune your AI Skill with: ? AI Instructions – Define rules for AI query generation (e.g., use Power BI for finance queries, Lakehouse for raw sales data). ? Example Queries – Provide sample SQL/KQL queries to train the AI for better responses. ? Schema Metadata – Ensure tables and columns have clear, meaningful names (e.g., SalesData instead of Table1).

Step 5: Publish & Share

1?? Click Publish to make your AI Skill available to colleagues.

2?? Share access via Fabric’s built-in permissions.

3?? Users can now query Fabric using natural language, without technical expertise! ??

?? How AI Skill Works Under the Hood

When you ask a question, the AI Skill follows these steps: ? Step 1: Accesses data schema using user credentials. ? Step 2: Constructs a natural language prompt with metadata, sample queries, and user input. ? Step 3: Chooses the correct AI tool:

• NL2SQL (Natural Language to SQL) → For querying warehouses & lakehouses.
• NL2DAX (Natural Language to DAX) → For Power BI semantic models.
• NL2KQL (Natural Language to KQL) → For KQL-based analytics. ? Step 4: Runs the query, retrieves results, and presents them in a conversational format. ? Step 5: Shows the query execution steps & generated code for transparency.

?? Why This is a Game-Changer for Business Intelligence?

Microsoft Fabric AI Skills unlock powerful, self-service analytics for organizations: ? Empowers non-technical users to explore data without SQL knowledge. ? Accelerates decision-making by providing instant data insights. ? Enhances productivity by reducing manual query building. ? Ensures data security by maintaining user access control within Fabric.

With AI-powered RAG capabilities, Microsoft Fabric makes enterprise AI accessible, accurate, and intuitive. ??

?? Key Takeaways

?? Microsoft Fabric AI Skills allow anyone to create a RAG solution over structured data sources.

?? 100% Low-Code – No advanced coding or AI expertise required.

?? Enables self-service analytics for business teams, reducing reliance on data engineers.

?? Supports lakehouses, warehouses, Power BI, and KQL for AI-powered querying.

?? Customizable – Fine-tune with instructions, schema metadata, and example queries.

?? Secure & Transparent – Uses Azure OpenAI API while maintaining data integrity.

?? Ready to Try This? Let’s Discuss!

What do you think about Microsoft Fabric AI Skills for AI-powered RAG solutions? Would this help your organization unlock more value from data? Let’s discuss in the comments! ????

#MicrosoftFabric #AI #RAG #DataScience #BusinessIntelligence #LowCode #Copilot #MachineLearning #Azure #DataAnalytics #PowerBI #AIinBI