7 Free Courses to Master RAG

Retrieval-Augmented Generation (RAG) is one of the most popular techniques for enhancing the accuracy of LLMs. It retrieves relevant external documents or data to create contextually appropriate answers.

We have already made a list of 12 different RAG types in one of our previous Twitter Library episodes, but researchers are continually developing more variations of RAG, increasing its effectiveness (there's no end to it!). So now it's time for a deep dive into studying and better understanding RAG systems.

Here are 7 free courses that can help you master RAG:

1. Retrieval Augmented Generation for Production with LangChain & LlamaIndex

   This course from Activeloop platform includes 43 lessons with 7+ hands-on practical projects that will help you master advanced tools like LangChain, LlamaIndex and Deep Memory. It explains basic concepts and components of RAG, advanced techniques like fine-tuning, dives into RAG Agents, evaluation and observability of RAG. This course is perfect if you're building a chat with data app or exploring how to use Generative AI in industries.

2. Introduction to Retrieval Augmented Generation (RAG) by Duke University:

   This 2-hour Coursera guided project course will teach you how to build an end-to-end RAG system with your own data, using open source tools, such as Pandas, SentenceTransformers and Qdrant for importing data and an LLM like Llamafile or OpenAI.

3. Knowledge Graphs for RAG by DeepLearningAI together with Neo4j:

   It will teach you how to use knowledge graphs in RAG applications. Through video lessons and code examples you will explore how knowledge graphs represent data with nodes and edges, and advanced techniques for correcting graphs. You will also use Neo4j's Cypher to query movie and actor data, and build a knowledge graph from financial documents.

4. RAG++ : From POC to Production by Weights & Biases in collaboration with Cohere and Weaviate:

   It gives practical RAG and RAG evaluation techniques for engineers for consistent and reliable outputs while minimizing hallucination and costs. This course provides 76 lessons with video content and Cohere credits to run course notebooks.

5. Building Multimodal Search and RAG by DeepLearningAI:

   You will build multimodal RAG systems to retrieve and process diverse data types for improved responses, and explore applications like multimodal search and develop multi-vector recommender systems for personalized recommendations.

6. Building Agentic RAG with LlamaIndex by DeepLearningAI:

   This course is designed for beginners. You will learn to build a RAG agent for document analysis and complex question answering, create a router agent for tasks like Q&A and summarization, and design a research agent for multi-document work with effective debugging and control methods.

7. Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API by Google Cloud:

   Here you will learn to extract and store metadata from documents with text and images, generate embeddings, and use text or image queries to search for similar content. You will also explore how to retrieve contextual answers by leveraging both text and images for comprehensive results.