10 RAG Courses in 2026: Free, Trial, and Paid Options

Updated September 11, 2026

We checked every enrollment page before this update. “Free” below means the learning material can be accessed without paying; it does not necessarily include a certificate, graded assignments, API usage, or cloud credits. Prices and beta access can change.

The strongest current options come from DeepLearning.AI, Weights & Biases, Activeloop, Duke University, Google Cloud, and Pinecone. Together they cover RAG fundamentals, retrieval and reranking, evaluation, knowledge graphs, multimodal systems, agentic RAG, and production practice. The right starting point depends on whether you want a free introduction, a short guided project, or a longer certificate program.

No. Two options are fully free, several DeepLearning.AI courses are free during a limited beta, two Coursera projects require a purchase, subscription, or trial, and Activeloop’s course is paid. We kept the paid entries because they were in the original list or offer something distinctive, but the table makes the difference explicit.

Some do. Activeloop includes certification, and the Duke and Google Cloud Coursera projects offer shareable certificates with paid access. DeepLearning.AI course accomplishments require PRO. Weights & Biases and Pinecone do not advertise certificates for the resources listed here.

Retrieval-Augmented Generation, or RAG, is a method that gives a language model relevant information from an external source before it answers. Instead of relying only on what the model learned during training, a RAG system searches documents, databases, images, or other sources and places the most useful evidence in the model’s context. Modern systems often add reranking, evaluation, knowledge graphs, multimodal retrieval, and agents that can run several searches. Our guide to 20 advanced RAG types to know in 2026 maps those variants.

Here are 10 current options. Availability was checked on September 11, 2026.

This is the most complete course in the list: roughly 24.5 hours, with 49 videos, code examples, and graded work. It moves from retrieval basics to vector databases, evaluation, deployment, and newer RAG patterns. The first module is available as a free preview; the full course and certificate require DeepLearning.AI PRO. Choose it if you want a curriculum rather than a quick tutorial.

This remains the strongest genuinely free production-oriented option here. Its 76 lessons and roughly two hours of video cover retrieval quality, evaluation, observability, hallucination control, and cost. It includes Cohere credits for the notebooks. A certificate is not advertised, and the course assumes Python plus a basic understanding of RAG.

Activeloop’s course is still available, but it is no longer free: the listed price was $199 when we checked. The program has 35 lessons, about 25 hours of learning, more than seven projects, and a certificate. It covers LangChain, LlamaIndex, Deep Memory, evaluation, observability, and RAG agents. It suits readers who want a longer, project-heavy certification path.

This two-hour guided project builds an end-to-end RAG system with tools such as Pandas, SentenceTransformers, and Qdrant. It is concise and practical, but Coursera guided projects are not freely auditable: access normally comes through a purchase, Coursera Plus, or a trial. The project offers a shareable certificate.

This short course focuses on a part of RAG that tutorials often skip: how to tell whether retrieval and generation are actually working. In about two hours, it covers advanced retrieval and practical evaluation through videos and code examples. Access is free for a limited time during beta; the course accomplishment requires PRO.

This course teaches how to represent relationships as nodes and edges, query them with Cypher, and build a knowledge graph from documents. It is especially useful when questions depend on connections across entities rather than simple semantic similarity. The course takes about two hours and is free during its limited beta; the course accomplishment requires PRO.

Here you build retrieval systems that work across text and images, including multimodal search and multi-vector recommendations. The course has eight lessons and six code examples and takes about 80 minutes. It is free during the limited beta; the course accomplishment requires PRO.

This 44-minute beginner course shows how a RAG agent can route between question answering and summarization, work across several documents, and support a research workflow. It is a useful bridge from a fixed retrieval pipeline to an agent that decides which tool or source to use. Access is free during the limited beta; the course accomplishment requires PRO.

This hands-on Coursera project uses text and image metadata, embeddings, and Gemini on Vertex AI to retrieve context from multimodal documents. It takes under two hours and offers a shareable certificate. Access is through a Coursera purchase, Plus subscription, or trial rather than a permanent free audit.

Pinecone’s free, self-paced series is a useful reference if you prefer reading and short videos to a formal course. It covers embeddings, rerankers, retrieval, and evaluation, although some later chapters are still marked “coming soon.” There is no certificate, and any API or infrastructure costs for your own experiments are separate.

Start with Building Agentic RAG with LlamaIndex if you want a short practical introduction, or the free first module of DeepLearning.AI’s full RAG course if you want a structured foundation. Basic Python and familiarity with APIs will help.

At our September 11, 2026 check, RAG++ from Weights & Biases and Pinecone’s RAG Learning Series were fully free. Several DeepLearning.AI short courses were also free during a limited beta, but that access can change.

Usually not. Activeloop includes certification with its paid course, while the Duke and Google Cloud Coursera projects offer shareable certificates through paid access. DeepLearning.AI course accomplishments require PRO.

It should go beyond connecting an LLM to a vector database. Look for chunking, hybrid retrieval, reranking, evaluation, observability, source attribution, cost and latency trade-offs, and at least one of multimodal, GraphRAG, or agentic retrieval.

Yes. RAG remains a practical way to ground models in private, recent, or specialized information. The architecture is becoming more sophisticated, but reliable retrieval and evaluation are still central to enterprise search, support, research, and document workflows.