Best RAG Courses

Retrieval-augmented generation (RAG) is revolutionizing how Large Language Models (LLMs) interact with information. By grounding LLMs in external knowledge sources, RAG empowers them to generate more accurate, informative, and contextually relevant responses. This represents a significant advancement over traditional LLMs, which often struggle with knowledge cut-offs, hallucinations (generating plausible-sounding but incorrect information), and a lack of real-time information updates. As the field of AI rapidly evolves, understanding RAG is becoming increasingly important for developers, engineers, and data scientists who are building the next generation of AI applications.

One way to enhance LLMs is through fine-tuning, where a pre-trained model is refined on specific tasks using a smaller dataset. However, RAG offers several advantages over fine-tuning, such as:

• Dynamic Data Integration: RAG systems can access and process real-time information from external sources, making them more adaptable to new information and reducing the need for constant retraining.
• Reduced Hallucinations: By grounding responses in factual data, RAG helps minimize the risk of LLMs generating incorrect or nonsensical information.
• Improved Explainability: RAG systems can provide sources for their answers, increasing transparency and trust in the generated output.

This article reviews some of the best AI RAG courses available online, providing a detailed analysis of their content, pros and cons, and target audience. Whether you're a beginner looking for an introduction to RAG or an experienced AI practitioner seeking to deepen your knowledge, this review will help you find the perfect course to meet your needs.

Summary Table of Top AI RAG Courses

Course Name	Provider	Duration	Cost	Key Features	Target Audience
Retrieval Augmented Generation for Production with LangChain & LlamaIndex	Activeloop	20+ hours	Free	Hands-on projects, advanced tools, production focus	Aspiring AI professionals, executives, and enthusiasts
Introduction to Retrieval Augmented Generation (RAG)	Duke University (Coursera)	2 hours	Free	Guided project, open-source tools	Beginners
Knowledge Graphs for RAG	DeepLearningAI & Neo4j	1 hour 38 minutes	Free	Knowledge graph integration, Cypher	Anyone interested in knowledge graphs and RAG
RAG++: From POC to Production	Weights & Biases, Cohere, and Weaviate	2 hours	Free	Practical RAG techniques, evaluation, Cohere credits	Engineers
Building Multimodal Search and RAG	DeepLearningAI	1 hour 21 minutes	Free	Multimodal RAG, diverse data types	Anyone interested in multimodal AI and RAG
Building Agentic RAG with LlamaIndex	DeepLearningAI	46 minutes	Free	Agentic RAG, LlamaIndex	Anyone with basic Python knowledge
Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API	Google Cloud	2 hours	Free	Multimodal RAG, Gemini API, Vertex AI	Individuals familiar with basic Python and Vertex AI

Detailed Course Reviews

Retrieval Augmented Generation for Production with LangChain & LlamaIndex

Overview: This comprehensive course from Activeloop provides a deep dive into RAG, equipping learners with the skills to build production-ready applications. With over 20 hours of content, it covers a wide range of topics, from basic concepts to advanced techniques like fine-tuning and RAG agents.

Course Content:

• Basics of RAG: Introduction to LangChain and LlamaIndex, understanding the core components of RAG, and building a basic RAG pipeline.
• Advanced RAG Techniques: Deep dive into advanced retrieval methods, such as query expansion, transformation, reranking, and recursive retrieval. It also covers optimizing context embeddings and building multimodal search systems.
• RAG Agents: Learn how to build agents that can reason over documents, answer complex questions, and perform tasks like summarization and question answering.
• Production-Ready RAG: Explore techniques for optimizing RAG for production, including efficient data management, handling large datasets, and ensuring scalability. The course emphasizes hands-on learning with 7+ practical projects that allow you to apply your knowledge and build real-world applications, such as an AI agent that can plan an outfit based on clothing items, budget, and weather, and a medical pill recognizer that distinguishes similar-looking pills and provides pharmacological information.

Pros	Cons
Comprehensive and in-depth coverage of RAG	Requires a significant time commitment
Hands-on projects that provide practical experience
Focus on production-ready solutions
Free certification upon completion

Best Suited For: This course is designed for aspiring AI professionals, executives, and enthusiasts eager to apply AI in practical scenarios. It caters to a wide range of learners, from those with basic AI knowledge to those seeking to enhance their organization's AI capabilities.

Introduction to Retrieval Augmented Generation (RAG) by Duke University

Overview: This 2-hour guided project on Coursera offers a practical introduction to RAG, ideal for beginners. It guides you through the process of building an end-to-end RAG system using your own data and open-source tools like Pandas, SentenceTransformers, and Qdrant.

Course Content:

• Data Preparation: Learn how to prepare your data for RAG, including importing and cleaning data using Pandas.
• Building a RAG Pipeline: Step-by-step guide to building a RAG pipeline using open-source tools, including SentenceTransformers for generating embeddings and Qdrant for vector storage and retrieval.
• Evaluating RAG Systems: Basic techniques for evaluating the performance of your RAG system.

Pros	Cons
Concise and focused on practical application	May be too basic for those with prior AI or RAG experience
Suitable for beginners with no prior RAG experience	Limited coverage of advanced RAG techniques
Uses open-source tools, making it accessible to a wider audience

Best Suited For: This course is ideal for beginners with no prior experience in RAG who want to gain hands-on experience with real-world applications in a structured and supportive learning environment.

Knowledge Graphs for RAG

Overview: This free course from DeepLearningAI and Neo4j explores the use of knowledge graphs in RAG applications. Through video lessons and code examples, you'll learn how knowledge graphs represent data with nodes and edges, and how to use them to enhance the performance of LLMs in RAG systems.

Course Content:

• Knowledge Graph Fundamentals: Introduction to knowledge graphs, their structure, and how they represent data. Knowledge graphs are particularly useful for representing hierarchical information and complex relationships between entities.
• Querying Knowledge Graphs: Learn how to use Neo4j's query language, Cypher, to manage and retrieve data from knowledge graphs.
• Building RAG Applications with Knowledge Graphs: Explore how to integrate knowledge graphs into RAG pipelines to improve context retrieval and generate more relevant responses. This includes adding a vector index to a knowledge graph to represent unstructured text data and find relevant texts using vector similarity search. The course also covers building a knowledge graph of text documents from scratch and exploring advanced techniques for connecting multiple knowledge graphs and using complex queries for comprehensive data retrieval.

Knowledge graph embeddings can enhance RAG by capturing the semantic relationships between entities and concepts in the knowledge graph. These embeddings can improve retrieval accuracy and generate more contextually relevant responses.

Pros	Cons
Focuses on the important role of knowledge graphs in RAG	May require some prior knowledge of graph databases
Provides hands-on experience with Neo4j and Cypher	Limited coverage of other RAG techniques
Suitable for those with some familiarity with LangChain

Best Suited For: This course is designed for anyone who wants to understand how knowledge graphs work, how to build with them, and create better RAG applications.

RAG++: From POC to Production

Overview: This course from Weights & Biases, in collaboration with Cohere and Weaviate, focuses on practical RAG techniques for engineers. It provides 76 lessons with video content and Cohere credits to run course notebooks.

Course Content:

• Introduction to Advanced RAG: Overview of advanced RAG concepts and techniques, including the challenges and benefits of scaling RAG from proof of concept to production.
• Evaluation: Systematic RAG evaluation techniques for assessing performance and identifying areas for improvement.
• Data Ingestion and Preprocessing: Best practices for data ingestion and preprocessing to optimize retrieval and generation.
• Query Enhancement: Techniques for enhancing user queries to improve retrieval accuracy.
• Advanced Retrieval and Reranking: Explore advanced retrieval methods and reranking techniques to ensure the most relevant information is retrieved.
• Agentic RAG: Introduction to agentic RAG and how to build agents that can reason over documents and perform tasks.
• Response Synthesis and Prompting: Techniques for optimizing response synthesis and prompt engineering to generate high-quality outputs.
• Optimization for Speed and Efficiency: Strategies for optimizing RAG systems for speed and efficiency in production environments.

Scaling RAG from proof of concept to production involves addressing challenges related to performance, data management, risk, integration, and cost. Performance challenges include ensuring fast retrieval speeds and selecting the right chunks of information to feed to the LLM. Data management challenges involve handling large datasets, ensuring data quality, and maintaining version control. Risk challenges include mitigating hallucinations, bias, and toxicity in LLM outputs. Integration challenges involve seamlessly incorporating RAG into existing workflows. Cost challenges involve optimizing resource utilization and minimizing expenses associated with LLMs and vector databases.

The 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of the effects come from 20% of the causes. In the context of RAG development, this principle can be applied to prioritize development efforts by focusing on the 20% of features or improvements that will have the greatest impact on performance and user experience.

Pros	Cons
Focuses on practical RAG techniques for engineers	May be fast-paced for beginners
Provides systematic evaluation techniques	Limited coverage of theoretical concepts
Includes Cohere credits to run course notebooks

Best Suited For: This course is best suited for engineers looking to optimize and productionize RAG applications.

Building Multimodal Search and RAG

Overview: This course from DeepLearningAI explores the world of multimodal RAG, where you'll learn to build systems that can retrieve and process diverse data types, including text, images, and more.

Course Content:

• Multimodal Embeddings: Learn how to generate embeddings for different data modalities, such as text and images, and combine them for multimodal search.
• Multimodal Retrieval: Explore techniques for retrieving relevant information from multimodal knowledge bases, including using multimodal embeddings like CLIP to embed images and text together or using a multimodal LLM to produce text summaries from images.
• Multimodal Generation: Learn how to generate responses that incorporate information from multiple modalities.

There are two main approaches to building multimodal RAG pipelines:

1. Embed all modalities into the same vector space: This approach involves using a single embedding model to generate embeddings for all data modalities, such as text, images, and audio. This allows for efficient retrieval across different modalities but may require careful selection of an embedding model that can effectively capture the semantic meaning of diverse data types.
2. Ground all modalities into one primary modality: This approach involves converting all data modalities into a single primary modality, such as text. This can simplify the retrieval process but may result in some information loss during the conversion process.

Multimodal RAG enables more sophisticated inferences by combining information from different modalities. For example, a multimodal RAG system can analyze both the text and images in a document to provide a more comprehensive understanding of the content.

Pros	Cons
Focuses on the emerging field of multimodal RAG	May require some prior knowledge of machine learning and deep learning
Provides a comprehensive overview of multimodal AI concepts
Suitable for those with basic Python knowledge and familiarity with RAG

Best Suited For: This course is for anyone who wants to start building their own multimodal applications.

Building Agentic RAG with LlamaIndex

Overview: This 46-minute short course from DeepLearningAI focuses on building agentic RAG with LlamaIndex. You'll learn how to build agents that can reason over documents, answer complex questions, and perform tasks like summarization and question answering.

Course Content:

• Router Query Engine: Learn how to build a router agent that can select the appropriate query engine for a given task, such as choosing between a question-answering engine and a summarization engine.
• Tool Calling: Explore how to use LLMs to infer arguments and call tools within an agentic RAG system.
• Building an Agent Reasoning Loop: Learn how to build agents that can reason over multiple steps and make decisions.
• Building a Multi-Document Agent: Extend the research agent to handle multiple documents and perform more complex tasks.

Agentic RAG is particularly well-suited for complex question answering that requires multi-step reasoning and tool usage. It allows LLMs to act as intelligent agents that can navigate through documents, summarize information, and even formulate follow-up questions to provide comprehensive answers.

Semantic caching is a technique used in agentic RAG to store answers to recent queries alongside their semantic context. This allows the system to efficiently address similar requests without repeated LLM calls, leading to faster response times and consistent information delivery.

Pros	Cons
Concise and focused on building agentic RAG systems	May be too advanced for beginners with no prior RAG experience
Provides hands-on experience with LlamaIndex	Limited coverage of other RAG techniques
Suitable for those with basic Python knowledge

Best Suited For: This course is designed for anyone who has basic Python knowledge and wants to learn how to quickly build agents that can reason over their own documents.

Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API

Overview: This course from Google Cloud explores multimodal RAG using the Vertex AI Gemini API. You'll learn how to extract and store metadata from documents with text and images, generate embeddings, and use text or image queries to search for similar content.

Course Content:

• Introduction to Multimodal RAG: Overview of multimodal RAG concepts and the advantages of using the Vertex AI Gemini API.
• Building a Multimodal RAG System: Step-by-step guide to building a multimodal RAG system using the Gemini API, including how to extract and store metadata from documents with text and images, generate embeddings for text and images, and use text and image queries to search for similar content in a multimodal knowledge base.
• Retrieving Contextual Answers: Explore how to retrieve contextual answers by leveraging both text and images.

Multimodal RAG offers several advantages over text-based RAG:

• Enhanced knowledge access: Multimodal RAG can access and process both textual and visual information, providing a richer and more comprehensive knowledge base for the LLM.
• Improved reasoning capabilities: By incorporating visual cues, multimodal RAG can make better-informed inferences across different types of data modalities.

Multimodal RAG can be used to build applications that combine text and image understanding, such as:

• Visual product search: Users can upload images to find similar products in an e-commerce platform.
• Medical image interpretation: AI can analyze medical images, such as X-rays or scans, and provide insights to assist healthcare professionals.

Pros	Cons
Focuses on the powerful capabilities of the Vertex AI Gemini API	May require some prior knowledge of Google Cloud Platform
Provides hands-on experience with building a multimodal RAG system
Suitable for those with basic Python knowledge and familiarity with Vertex AI

Best Suited For: This course is designed for individuals familiar with basic Python programming, general API concepts, and running Python code in a Jupyter notebook on Vertex AI Workbench.

Conclusion

The field of AI RAG is rapidly evolving, with new techniques and applications emerging constantly. The courses reviewed in this article provide a comprehensive overview of the key concepts and tools in this exciting field. By carefully considering your learning goals and experience level, you can choose the course that best suits your needs and helps you master the art of retrieval-augmented generation.

Here are some recommendations for different types of learners:

• Beginners: The "Introduction to Retrieval Augmented Generation (RAG)" course by Duke University provides a gentle introduction to RAG with a focus on hands-on experience.
• In-depth Learners: The "Retrieval Augmented Generation for Production with LangChain & LlamaIndex" course from Activeloop offers a comprehensive and in-depth exploration of RAG, ideal for those seeking to build production-ready applications.
• Specific Interests: If you're interested in specific aspects of RAG, such as knowledge graphs or agentic RAG, the DeepLearningAI courses on "Knowledge Graphs for RAG" and "Building Agentic RAG with LlamaIndex" are excellent choices.
• Engineers: The "RAG++: From POC to Production" course from Weights & Biases is tailored for engineers who want to optimize and productionize RAG applications.
• Multimodal Learners: For those interested in multimodal RAG, the DeepLearningAI course on "Building Multimodal Search and RAG" and the Google Cloud course on "Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API" provide valuable insights and practical experience.

Choosing the right chunk size is crucial for RAG performance. Smaller chunks allow for more precise matching between queries and content, while larger chunks provide more context but may include irrelevant information.