Master RAG for AI + DevOps

In this session, we explore how Retrieval Augmented Generation (RAG) can be applied in the world of DevOps. You will first understand the concept of RAG, its importance in overcoming the limitations of Large Language Models (LLMs), and then see a live project where RAG is implemented with Kubernetes and Docker.

We cover key topics such as document loaders, chunking, embeddings, vector search, LLM augmentation, and more while addressing real challenges that DevOps teams face, like troubleshooting containers and managing rapidly changing configurations.

By the end of the session, you will have both the conceptual clarity and a practical demonstration of how RAG can enhance problem-solving in DevOps and beyond.

In this session, we break down the core workflow of Retrieval Augmented Generation (RAG). You will learn how the three key components the user query, the knowledge base, and the LLM (Gemini in this demo) — interact through a retriever program. We discuss how RAG enables LLMs to answer questions using private, real-time, and domain-specific data, going beyond what they already know from training.

The session also covers the practical steps of setting up retrieval with Python:

• Understanding the role of the retriever in query processing

• Connecting to private knowledge bases (documents, databases, or real-time data)

• Implementing a document loader in Python to access local/project files

• Preparing data to feed into the RAG pipeline for augmentation

By the end of this session, you will clearly understand how retrievers fetch relevant documents and how to build the first step of your own RAG-powered DevOps or enterprise AI solution.

In this session, we dive into the core steps of Retrieval Augmented Generation (RAG) — focusing on how to prepare and process data before sending it to an LLM. You will learn how documents are loaded, broken into smaller chunks, and then converted into numerical representations (embeddings) that LLMs can actually understand.

We will cover:

• Loading and structuring documents for RAG

• The role of retrievers in accessing data

• Tokenization and converting text into embeddings

• Why embeddings are stored as vectors and how normalization works

• Chunking large documents for better performance and accuracy

• Using Gemini’s embedding model (with API keys) in practice

By the end of this session, you will understand how to transform raw text documents into embeddings and vectors, which is the foundation for efficient semantic search and context-aware AI applications in RAG.

In this session, we explore how vector databases and similarity search power Retrieval Augmented Generation (RAG). You will learn how embeddings make it possible to search by meaning rather than exact words, enabling context-aware answers from large document sets.

We will cover:

• Why vector databases (Pinecone, Weaviate, FAISS, etc.) are used in RAG

• Storing and retrieving vectors efficiently for large-scale data

• How cosine similarity search finds relevant chunks of text

• Writing a retriever function in Python to query embeddings

• Feeding retrieved context into Gemini for policy-restricted, real-time answers

By the end of this session, you will understand how retrievers and similarity search connect your queries to the right context, making RAG accurate, scalable, and enterprise-ready.