Master RAG: Build a System to Chat With Your Data in Databricks

Learn how to build a RAG system to chat with your own data using a real, enterprise-ready workflow in Databricks.

In this video, we explain Retrieval-Augmented Generation (RAG) step by step and demonstrate how to build a production-style “chat with your data” system using modern data and AI tools.

You’ll learn how to:
• Upload and process documents (PDFs and text data)
• Split documents into chunks for accurate retrieval
• Generate embeddings automatically
• Perform semantic search using Databricks Vector Search
• Use a serverless LLM endpoint to generate grounded, reliable answers
• Build a RAG pipeline that avoids hallucinations
• Integrate Python, PySpark, SQL, dbt, Delta Lake, and databases into an enterprise AI workflow

This tutorial is ideal for:
• AI Engineers
• Data Engineers
• Machine Learning Engineers
• Analytics Engineers
• Anyone learning Generative AI, RAG systems, vector databases, and document AI

Key concepts explained:
Retrieval-Augmented Generation (RAG), embeddings, chunking, vector search, vector databases, LLMs, semantic search, AI pipelines, Databricks, Python, PySpark, SQL, dbt, Delta Lake, enterprise AI, GenAI, MLOps

Links & Resources:
• Portfolio: https://benjaminuka.streamlit.app/
• GitHub: https://github.com/uka-ben
• LinkedIn:   / benjamin-uka-imo  
• Email: benjaminukaimo@gmail.com


#databricks
#datascience
#ai
#MLOps
#data


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