Master RAG for AI + DevOps

Build AI that Searches, Reads & Responds with Your Data

What you'll learn
- Understand Retrieval Augmented Generation (RAG) architecture.
- Implement document loading and preprocessing for AI models.
- Apply chunking techniques to handle large-scale documents.
- Work with vector databases for similarity search and retrieval.
- Using RAG to enhance LLM performance with domain-specific data.
- Integrate retrievers to connect user queries with relevant context.

Requirements
- No prior knowledge of RAG or LLMs needed we’ll teach you everything step by step.

Description
This course provides a complete, step-by-step journey into Retrieval Augmented Generation (RAG) and its practical applications. You will learn how to prepare data, build retrievers, use vector databases, and connect everything with large language models (LLMs) like Gemini to create powerful, context-aware AI systems.

The course is designed for learners who want both conceptual clarity and hands-on implementation, especially in domains such as DevOps and enterprise AI. Each session builds on the previous one to gradually help you master the RAG workflow from fundamentals to advanced topics.

What you will learn:

Session 1: RAG for DevOps: Document Loaders, Chunking, Embeddings & Vector Search

Understand the importance of RAG in overcoming LLM limitations. Learn document loaders, chunking, embeddings, and vector search through real-world DevOps examples such as troubleshooting containers and managing dynamic configurations.

Session 2:RAG Workflow: Queries, Retrievers, Knowledge Base & Python Integration

Explore the core RAG workflow. Learn how queries, retrievers, and knowledge bases interact with LLMs. Build retrievers in Python, load local documents, and connect to private knowledge bases to extend LLM capabilities.

Session 3:Preparing Data for RAG: Chunking Documents & Creating LLM Embeddings

Learn how to transform text into embeddings for semantic search and AI applications. Cover document structuring, tokenization, normalization, and chunking strategies. Implement embeddings using Gemini’s API for efficient data retrieval.

Session 4:Vector Databases, Similarity Search & Retrievers in RAG with Gemini

Discover how vector databases enable scalable and efficient retrieval. Learn about Pinecone, Weaviate, and FAISS, implement cosine similarity search, write retriever functions in Python, and connect results to Gemini for accurate answers.

By the end of this course, you will:

Understand the complete workflow of RAG and how it augments LLMs.

Build retrievers and use vector databases for real-world projects.

Apply RAG concepts to DevOps and enterprise AI scenarios.

Gain hands-on experience with Python, embeddings, and Gemini.

This course is ideal for developers, DevOps engineers, data professionals, and AI enthusiasts who want to build practical RAG-powered solutions for real-world challenges.

Who this course is for:
- DevOps engineers exploring AI and RAG
- Data scientists working with LLMs
- AI/ML enthusiasts interested in RAG
- Software developers building with Gemini & Python
- Students curious about AI-powered search
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