Most people know the buzzwords — RAG, embeddings, vector databases, LLMs — but struggle to understand how everything actually works together in production.
Databricks Gen AI (2026 Edition) is built to solve that. This ebook breaks down Generative AI, RAG pipelines, Vector Search, Embeddings, Prompt Engineering, and Databricks workflows in a structured, practical, and beginner-friendly way — so you don’t just memorize concepts, you understand how modern AI systems are built end-to-end.
Perfect for:
• Beginners entering Generative AI & LLM engineering
• Data scientists exploring production AI systems
• Data engineers learning RAG workflows
• AI enthusiasts preparing for Gen AI interviews
• Professionals transitioning into AI/ML roles
If you want Gen AI to finally make practical sense, this ebook is built for you.
What You’ll Learn
Inside this ebook:
• Foundations of Databricks & Lakehouse AI
• Prompt Engineering frameworks & techniques
• RAG (Retrieval-Augmented Generation) architecture
• Embeddings, vectorization & similarity search
• Chunking strategies for production-grade RAG
• Vector Databases & Mosaic AI Vector Search
• Delta Tables, Unity Catalog & data governance
• Databricks LLM setup & model serving
• Re-ranking strategies for accurate retrieval
• End-to-end RAG workflows with real examples
• Practical interview-focused Gen AI concepts
The content follows a structured progression — from fundamentals to advanced production workflows — with diagrams, notes, workflows, and real implementation concepts throughout the book.
Created by a Senior Data Scientist, this ebook focuses on:
• Strong conceptual clarity over hype
• Real production workflows over theory-only learning
• Practical understanding over memorization
• Interview-ready explanations with visual learning
Every topic is simplified step-by-step so you can confidently understand modern AI systems and how companies actually implement Gen AI solutions.
Generative AI is transforming every industry.
Learn how modern AI applications are built, understand RAG pipelines deeply, and build the foundations needed for AI engineering, data science, and LLM applications.
