RAG Mastery: Build AI Apps with Your Own Data

Course Overview

This course provides a comprehensive introduction to Retrieval-Augmented Generation (RAG) and its application in building AI systems that leverage your own data. It covers foundational concepts such as large language models (LLMs), embeddings, vector databases, semantic search, and the architecture of RAG pipelines. Learners will gain practical skills in document processing, retrieval techniques, evaluation, and optimization of RAG systems to create production-ready AI applications.

Key Takeaways

• Understand the principles and architecture of Retrieval-Augmented Generation and its role in modern AI applications.
• Build knowledge of LLMs, embeddings, vector databases, and semantic search.
• Design and implement complete RAG pipelines connecting documents and data with language models.
• Learn advanced retrieval techniques including hybrid search, reranking, filtering, and query enhancement.
• Evaluate and optimize RAG systems using metrics, testing datasets, and production workflows.
• Develop production-ready AI applications such as enterprise assistants, document search, and intelligent chatbots.

Prerequisites

No prior knowledge of Retrieval-Augmented Generation or Large Language Models is required. Basic programming knowledge is helpful but not mandatory. A curiosity about AI applications and willingness to learn modern AI technologies is recommended.

Target Learners

• Developers and software engineers interested in building AI applications using LLMs and private data.
• AI engineers and machine learning practitioners aiming to implement RAG systems.
• Beginners wanting to understand modern AI applications, embeddings, vector databases, and LLM systems.
• Professionals seeking to create enterprise AI assistants, document search systems, and knowledge-based chatbots.
• Anyone interested in learning key technologies behind modern AI applications.