7 Free RAG Courses in 2026: Agentic, Multimodal & Production

Updated July 18, 2026

(All courses were available free or with a free learning option at the time of publication. Certificates, graded assignments, cloud credits, and full Coursera access may require payment.)

The best free RAG courses in 2026 are offered by DeepLearning.AI, Google Cloud, Weights & Biases, Activeloop, and Duke University. They cover agentic RAG with LlamaIndex, multimodal retrieval with Weaviate and Gemini, knowledge graphs with Neo4j, and production systems with Cohere. Most courses do not require advanced machine learning knowledge — basic Python experience and familiarity with APIs are enough.

Retrieval-Augmented Generation, or RAG, is a method that helps large language models produce more accurate and up-to-date answers by retrieving relevant information from external sources before generating a response. A RAG system typically searches documents, databases, or other knowledge stores, adds the retrieved material to the model’s context, and asks the model to answer using that evidence. Modern RAG systems increasingly include reranking, multimodal retrieval, knowledge graphs, evaluation pipelines, and agents that can run multiple searches or choose between different retrieval tools.

RAG remains one of the most practical techniques for building AI applications with private, recent, or domain-specific information. The courses below cover the path from basic retrieval pipelines to production RAG, including agentic, multimodal, and knowledge graph-based architectures.

We have already created a guide to 20 advanced RAG types to know in 2026, but researchers and engineers continue to develop new retrieval and evaluation methods. So now it is time for a more practical deep dive. Below are seven courses for engineers and researchers who want to move beyond RAG theory and build working systems with tools such as LangChain, LlamaIndex, Neo4j, Weaviate, Qdrant, Cohere, and Google Vertex AI.

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

This free certification course from Activeloop includes 35 lessons and more than seven practical projects. It covers RAG fundamentals, LangChain, LlamaIndex, Deep Memory, evaluation, observability, and techniques for building production-oriented RAG applications. 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.

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.

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.

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.

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.

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.

Retrieval-Augmented Generation, or RAG, is a method that helps large language models produce more accurate and up-to-date answers by retrieving relevant information from external sources before generating a response. A RAG system typically searches documents, databases, or other knowledge stores, adds the retrieved material to the model’s context, and asks the model to answer using that evidence.

RAG is one of the most widely used techniques for building AI applications with private, recent, or domain-specific information. It can reduce unsupported answers, connect models to proprietary data, and make it possible to update an application’s knowledge without retraining the underlying model. RAG is commonly used in enterprise search, customer support, research assistants, document analysis, and internal knowledge tools.

The courses can be accessed free of charge through their primary learning platforms or through free enrollment options available at the time of publication. However, Coursera may require a trial, subscription, or financial-aid application for full access to some guided projects and certificates. Platform terms can change, so check the enrollment page before registering.

Certificate availability depends on the platform. Activeloop offers a certification path for its RAG course, while DeepLearning.AI Short Courses generally provide a course accomplishment rather than a formal professional certificate. Coursera projects may include shareable certificates, but receiving one can require a paid subscription, trial, or financial aid. Free access to the learning material does not always include a free certificate.

Building Agentic RAG with LlamaIndex by DeepLearning.AI is a beginner-friendly introduction to creating RAG agents for document analysis, question answering, and multi-document research. The Duke University guided project is another accessible option for learners who want to build a basic end-to-end RAG pipeline with step-by-step instructions.

RAG++: From POC to Production by Weights & Biases, Cohere, and Weaviate is the most production-focused course in the list. It covers RAG evaluation, output reliability, hallucination reduction, and cost management. Activeloop’s Retrieval Augmented Generation for Production course also covers evaluation, observability, agents, and production-oriented RAG workflows.

Standard RAG usually performs one retrieval step and passes the selected information to a language model. Agentic RAG allows an AI agent to plan and repeat retrieval, reformulate queries, select different tools, evaluate retrieved information, and decide when it has enough evidence to answer. It is better suited to complex, multi-step tasks, but it is also more difficult to control and evaluate.

Knowledge graphs represent information as entities and relationships rather than only as chunks of text. In a RAG system, they can improve retrieval for questions involving connected people, companies, products, events, or concepts. They are particularly useful when relationships between facts are as important as the facts themselves.

Knowledge graph RAG is useful for structured or highly interconnected domains such as finance, healthcare, legal research, scientific literature, supply chains, and organizational knowledge. It can outperform plain vector retrieval when a question requires following relationships across multiple entities or documents.

Multimodal RAG retrieves and processes more than one type of data, such as text, images, charts, tables, audio, or video. Instead of searching only written passages, a multimodal system can find relevant visual or non-text information and use it when generating an answer.

Building Multimodal Search and RAG by DeepLearning.AI and Weaviate covers multimodal retrieval, multi-vector search, and recommendation systems. Google Cloud’s multimodal RAG course focuses on extracting information from documents containing text and images, generating embeddings, and retrieving relevant context with Gemini-based tools.

Most of the courses do not require advanced machine learning knowledge. Basic Python experience and familiarity with APIs are enough for the beginner courses. Production-focused courses are easier to follow if you already understand embeddings, vector databases, LLM APIs, and basic RAG architecture.

Across the seven courses, learners can work with tools including LangChain, LlamaIndex, Qdrant, Weaviate, Neo4j, Cypher, SentenceTransformers, Cohere, OpenAI APIs, Google Cloud, Gemini, and Pandas. The exact stack differs by course, but together they cover data ingestion, embeddings, retrieval, reranking, evaluation, agents, and deployment.

Course length varies considerably. Some DeepLearning.AI Short Courses can be completed in under two hours, while the Duke University guided project takes roughly two hours. Longer programs from Activeloop and Weights & Biases include dozens of lessons and practical exercises and may require several days of study.

Yes. Some courses use open-source models, local inference tools, or free platform credits. However, certain exercises may depend on OpenAI, Cohere, Google Cloud, or another commercial API. Check the course requirements before starting, because free course access does not necessarily include unlimited API or cloud usage.

Yes. RAG remains a core architecture for applications that need access to private, changing, or specialized information. The field is evolving beyond simple vector search toward agentic retrieval, multimodal systems, knowledge graphs, reranking, hybrid search, and stronger evaluation. Understanding basic RAG is still the foundation for working with these more advanced systems.