Advanced RAG from edX

Transform your ability to build enterprise-grade AI applications using advanced Retrieval-Augmented Generation (RAG) techniques. This comprehensive course is designed for AI engineers, MLOps professionals, and software developers seeking to master LLM implementation.

Key Course Features:

Hands-on labs and practical exercises with real-world applications
Deep dive into embedding vectors and document processing strategies
Advanced retrieval techniques including hybrid search and multimodal retrieval
Interactive weekly Q&A sessions with industry experts
Lifetime access to course materials and video recordings

Perfect for:

AI and MLOps engineers
Solutions architects
Enterprise architects
Software developers working with AI
Technical professionals transitioning to AI development

Prerequisites:

Basic understanding of AI/ML concepts
Programming experience
Familiarity with Python

What's inside

Learning objectives

Implement enterprise-grade rag systems from scratch
Master vector databases and embedding techniques
Design effective document chunking strategies
Create semantic search implementations
Build hybrid search and reranking systems

Develop multimodal retrieval solutions
Optimize query-document alignment techniques
Deploy contextual retrieval systems
Implement advanced document enrichment strategies
Create production-ready llm applications

Implement enterprise-grade rag systems from scratch
Master vector databases and embedding techniques
Design effective document chunking strategies
Create semantic search implementations
Build hybrid search and reranking systems
Develop multimodal retrieval solutions
Optimize query-document alignment techniques
Deploy contextual retrieval systems
Implement advanced document enrichment strategies
Create production-ready llm applications

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Focuses on RAG (Retrieval-Augmented Generation) techniques, which are essential for building enterprise-grade AI applications and are highly sought after in the AI field

Features hands-on labs and practical exercises with real-world applications, providing valuable experience for professionals looking to apply RAG techniques in their work

Explores embedding vectors and document processing strategies, which are fundamental for implementing effective RAG systems and improving the accuracy of information retrieval

Requires a basic understanding of AI/ML concepts and familiarity with Python, suggesting that learners should have some prior experience in the field before taking this course

Covers advanced retrieval techniques, including hybrid search and multimodal retrieval, which are cutting-edge approaches for enhancing the performance of RAG systems

Teaches how to deploy contextual retrieval systems, which are crucial for building production-ready LLM applications that can understand and respond to user queries effectively

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Advanced RAG with these activities:

Review Vector Databases

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Refresh your understanding of vector databases and embedding techniques to prepare for the course's deep dive into these topics.

Browse courses on Vector Databases

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Read introductory articles on vector databases.
Explore different vector database options (e.g., Pinecone, Weaviate, Chroma).
Review the concept of embeddings and their use in semantic search.

Experiment with Document Chunking

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Practice different document chunking strategies to understand their impact on retrieval performance.

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Select a sample document (e.g., a research paper, a blog post).
Implement different chunking methods (e.g., fixed-size, semantic).
Evaluate the quality of the chunks for semantic meaning.

Natural Language Processing with Transformers

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Review this book to gain a deeper understanding of the transformer models that power many RAG systems.

View Natural Language Processing with Transformers,... on Amazon

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Read the chapters on transformer architecture and attention mechanisms.
Study the examples of using transformers for text generation and question answering.
Consider how the concepts apply to RAG pipelines.

Four other activities

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Build a Simple RAG Pipeline

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Start a small project to build a basic RAG pipeline, solidifying your understanding of the core concepts.

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Choose a dataset of documents (e.g., Wikipedia articles, news articles).
Implement a basic retrieval mechanism using embeddings and a vector database.
Integrate a language model to generate responses based on retrieved documents.
Evaluate the performance of your RAG pipeline.

Designing Machine Learning Systems

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Review this book to understand the system design considerations for deploying RAG applications in production.

View AI Engineering: Building Applications with... on Amazon

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Read the chapters on model deployment and monitoring.
Consider how the concepts apply to RAG pipelines.
Think about the challenges of scaling RAG systems.

Blog Post: Advanced RAG Techniques

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Write a blog post summarizing advanced RAG techniques learned in the course to reinforce your understanding and share your knowledge.

Browse courses on Retrieval Augmented Generation

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Choose a specific advanced RAG technique (e.g., hybrid search, multimodal retrieval).
Research the technique and gather relevant information.
Write a clear and concise explanation of the technique.
Include examples and illustrations to enhance understanding.

Contribute to a RAG-related Open Source Project

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Contribute to an open-source project related to RAG to gain practical experience and collaborate with other developers.

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Find an open-source project related to RAG (e.g., Langchain, LlamaIndex).
Identify a bug or feature to work on.
Submit a pull request with your changes.
Participate in code reviews and discussions.

Career center

Learners who complete Advanced RAG will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

The role of a machine learning engineer involves developing and deploying machine learning models and systems. This course, with its focus on implementing enterprise-grade Retrieval-Augmented Generation systems, directly aligns with the responsibilities of a machine learning engineer who is working on advanced applications of large language models. The practical exercises in document processing, vector embeddings, and retrieval are particularly helpful. A machine learning engineer benefits from this course by gaining a deeper understanding of advanced techniques in AI and RAG.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Engineer

An artificial intelligence engineer is responsible for creating and implementing AI solutions. This course in advanced retrieval-augmented generation provides AI engineers with the skills needed to deploy sophisticated AI applications. The hands-on labs and practical exercises, especially those focused on embedding vectors, document processing, and advanced retrieval techniques, directly translate to the challenges encountered by an artificial intelligence engineer. This course is particularly useful for those involved in building advanced large language model implementations.

See salaries and explore the career path for Artificial Intelligence Engineer

MLOps Engineer

The MLOps engineer role is crucial for deploying and managing machine learning models, and this course on advanced RAG provides a strong foundation for that. The course's emphasis on enterprise-grade systems, practical application, and deployment of contextual retrieval systems directly benefits MLOps engineers. The knowledge of document processing, vector databases, and query-document alignment techniques gained in this course ensures an MLOps engineer is well-versed in cutting-edge best practices. This is particularly helpful for any MLOps professional hoping to integrate LLMs into their organization.

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Solutions Architect

Solutions architects design and oversee the implementation of technology solutions to meet business needs. This course is useful to a solutions architect, especially with its deep dive into embedding vectors, document processing, and retrieval techniques. A solutions architect can leverage the knowledge gained here to design sophisticated AI systems for their organizations. The course, with its focus on enterprise-grade applications, is tailored for those who are defining robust and scalable AI solutions. By taking this course, a solutions architect can ensure their AI designs are aligned with modern best practices.

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Data Scientist

Data scientists analyze data and build models, and this course gives them a stronger ability to incorporate advanced RAG systems into their work. The focus on vector databases, embedding techniques, and document chunking is particularly valuable for data scientists. A data scientist can benefit from the course’s emphasis on optimizing query-document alignment and building contextual retrieval systems. This course helps a data scientist extend their modeling skills to include sophisticated, generative AI applications.

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Software Developer

A software developer builds software applications, and this course in advanced RAG provides essential knowledge for developing AI-powered applications. The course's focus on implementation, document processing strategies, and the creation of semantic search implementations directly benefit a software developer working with artificial intelligence. The course’s inclusion of hybrid and multimodal retrieval strategies ensures a software developer is equipped with the latest advancements in AI. Any software developer aiming to create sophisticated generative AI applications will find this course a very helpful resource.

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Research Scientist

Research scientists explore and develop new technologies, such as advanced artificial intelligence methods. This course on implementing enterprise-grade RAG systems is very useful for a research scientist, especially given its focus on document processing, embedding techniques, and retrieval methods. This course’s coverage of hybrid search and multimodal retrieval helps a research scientist contribute to cutting-edge advancements in AI. The course material is invaluable to any researcher who wants to delve into advanced information retrieval and language model applications.

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Natural Language Processing Engineer

A natural language processing engineer focuses on building systems that understand and process human language, and this course may be useful as it provides a deep dive into retrieval-augmented generation, a crucial technique for such systems. The course content on embedding vectors, semantic search, and query-document alignment is particularly relevant to the tasks of an NLP engineer. An NLP engineer will find the material presented in this course useful for building more powerful and efficient language models. By taking this course, an NLP engineer can greatly enhance their knowledge of the latest advancements.

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Technical Lead

Technical leads guide development teams and this course on implementing enterprise-grade Retrieval Augmented Generation systems may be useful. The course's focus on advanced retrieval techniques and deployment of contextual retrieval systems can help a technical lead stay on top of best practices in AI applications. A technical lead may also use the course content on embedding vectors, document processing, and semantic search to guide their team’s technology choices. This is useful for any technical lead wanting to gain exposure to the latest methods in large language models.

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AI Product Manager

An artificial intelligence product manager directs the strategy, roadmap, and execution of AI products. This course on advanced RAG techniques may be useful for an AI product manager, especially one who seeks a deeper understanding of the technical underpinnings of their products. The course's coverage of document chunking, semantic search, and hybrid search helps an AI product manager understand the capabilities and limitations of these systems. An AI product manager can leverage this knowledge to make more informed product decisions. This course is useful for product managers looking to stay ahead of artificial intelligence trends.

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Data Engineer

Data engineers design and build systems for collecting, storing, and processing data, and this course may be useful due to its relevance toward building systems for AI applications. A data engineer benefits from this course's coverage of vector databases and document processing strategies. The course’s focus on deployment of retrieval systems can be particularly relevant to a data engineer, especially one working in machine learning. This course is particularly useful for data engineers aiming to expand their role to include aspects of machine learning operations.

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Machine Learning Researcher

Machine learning researchers explore new algorithms, models, and approaches, and this course may be useful if they need to explore implementation details of advanced RAG systems. The focus on state-of-the-art retrieval techniques, multimodality, and query-document alignment provides practical experience that can guide a machine learning researcher’s theoretical work. Machine learning researchers may explore new techniques based on the course’s coverage of semantic search, hybrid search, and reranking. This course may help a machine learning researcher translate theoretical findings into practical results.

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Business Intelligence Analyst

A business intelligence analyst may find this course useful because it covers technologies that are relevant to data analysis. This course on retrieval augmented generation provides knowledge of how to perform advanced searches from data. This content may help a business intelligence analyst better leverage data for insights. The course's coverage of document processing and query-document alignment techniques may help develop analytical capabilities. This course may be helpful for a business intelligence analyst who wants to gain more advanced skills with data analysis.

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Database Administrator

A database administrator manages and maintains databases, and this course may be useful since it discusses vector databases. The course content regarding embedding techniques, and query-document alignment may help a database administrator who is looking to gain exposure to new data storage and retrieval techniques. A database administrator can leverage the knowledge gained here to better understand the demands of artificial intelligence applications. This is helpful for any database administrator who is curious about the future of data storage.

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Technical Writer

Technical writers create documentation for software and hardware, and this course may be useful to technical writers who document artificial intelligence products. A technical writer could use this course to better understand the core concepts behind advanced retrieval systems. The course material on vector embeddings, document chunking, and semantic search could be helpful for creating accurate and comprehensive documentation. This may be useful for those who wish to become more adept at documenting complex artificial intelligence applications.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Advanced RAG.

Natural Language Processing with Transformers,...

Save

Provides a comprehensive overview of transformers and their applications in NLP. It covers the underlying theory and practical implementation of transformer models, which are essential for understanding advanced RAG techniques. While not directly focused on RAG, it provides the necessary background on the LLMs that power RAG systems. This book is valuable as additional reading to deepen your understanding of the technology behind RAG.

Natural Language Processing with Transformers,...

Paperback

Natural Language Processing with Transformers,...

Kindle Edition

AI Engineering

Save

Focuses on the practical aspects of building and deploying machine learning systems, including considerations for scalability, reliability, and maintainability. While not specific to RAG, it provides valuable insights into the challenges of deploying LLM applications in production. It useful reference for understanding the system design aspects of RAG and how to build robust and scalable solutions. This book is commonly used by industry professionals.

AI Engineering: Building Applications with...

Kindle Edition

$$$

Advanced RAG

What's inside

Learning objectives

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Activities

Career center

Reading list

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