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. The courses below cover RAG training from fundamentals to production — including agentic, multimodal, and knowledge graph-based architectures.
We have already made a list of 20 advanced RAG types to know in 2026, 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. Below are 7 free courses selected for engineers and researchers who want to move beyond RAG theory — each includes hands-on projects, real production tools, and covers specific RAG architectures: from agentic and multimodal to knowledge graph-based systems.
Here are 7 free courses that can help you master RAG:
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.
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.
FAQ: Free RAG Courses
What is Retrieval-Augmented Generation (RAG) and why should I learn it?
Retrieval-Augmented Generation (RAG) is a technique that connects large language models to external knowledge sources — databases, documents, or live data — to generate accurate, grounded answers instead of hallucinated ones. Learning RAG is becoming essential for any engineer building production AI applications: it reduces hallucinations, allows LLMs to work with up-to-date data, and is the backbone of most enterprise AI pipelines in 2025–2026.
Are the RAG courses in this list truly free?
Yes — all 7 courses listed are free to access. The Activeloop course (LangChain & LlamaIndex), the DeepLearning.AI short courses on agentic RAG, multimodal RAG, and knowledge graphs for RAG, the Duke University Coursera guided project, and the Google Cloud Vertex AI RAG course are all available at no cost. Some Coursera courses require a free account but don't require a paid subscription to audit.
Which free RAG course is best for absolute beginners?
The Building Agentic RAG with LlamaIndex course by DeepLearning.AI is explicitly designed for beginners. It covers building a RAG agent step by step — from document analysis and Q&A to multi-document research agents — with clear debugging methods included. The Duke University Coursera project is also beginner-friendly at just 2 hours with guided, hands-on code.
Which course covers RAG for production environments?
RAG++ : From POC to Production by Weights & Biases (with Cohere and Weaviate) is the most production-focused option. Its 76 lessons cover RAG evaluation, hallucination reduction, and cost optimization — practical skills that go beyond proof-of-concept. The Activeloop RAG for Production with LangChain & LlamaIndex course (43 lessons, 7+ projects) also addresses production-grade RAG Agents and observability.
What is the difference between agentic RAG and standard RAG?
Standard RAG retrieves relevant documents once and passes them to the LLM to generate an answer. Agentic RAG uses an LLM agent that can iteratively refine retrieval queries, switch between tools (Q&A, summarization, web search), and handle complex multi-step reasoning across multiple documents. The Building Agentic RAG with LlamaIndex course covers this architecture specifically.
What are knowledge graphs for RAG and when do I need them?
Knowledge graphs represent data as nodes and relationships rather than flat text. In RAG applications, they improve retrieval accuracy for structured domains — financial documents, medical records, interconnected entities — where vector search alone loses relational context. The Knowledge Graphs for RAG course by DeepLearning.AI and Neo4j teaches Cypher querying and graph construction from real financial data.
What is multimodal RAG and which course covers it?
Multimodal RAG extends retrieval to images, charts, and other non-text data in addition to text. Two courses cover this: Building Multimodal Search and RAG (DeepLearning.AI, Coursera) and Multimodal RAG using the Vertex AI Gemini API (Google Cloud, Coursera). The first focuses on multi-vector recommender systems; the second on embedding extraction and cross-modal retrieval using Google's Gemini API.
Do I need prior machine learning knowledge to take these RAG courses?
Basic Python experience is sufficient for most courses. The Duke University course requires familiarity with Pandas; the Activeloop and DeepLearning.AI courses assume no ML background. The Weights & Biases RAG++ : From POC to Production course is better suited for engineers who already understand the basics of LLM APIs and vector databases.
What tools and frameworks will I learn across these RAG courses?
Across all 7 courses you'll gain hands-on experience with: LangChain, LlamaIndex, Qdrant, Weaviate, SentenceTransformers, Neo4j (Cypher), Google Vertex AI Gemini API, Cohere, OpenAI API, and Pandas. This covers the full production RAG stack — from data ingestion and chunking to vector search, evaluation, and deployment.
How long does it take to complete a free RAG course?
Course lengths vary significantly. The Duke University RAG course takes 2 hours. DeepLearning.AI short courses typically run 1–4 hours. The Activeloop course (43 lessons + 7 projects) and the Weights & Biases RAG++ : From POC to Production (76 lessons) require several days of study. For a complete foundation in RAG — from basics to multimodal and agentic systems — plan for 2–3 weeks working through multiple courses from this list.
