Course info

This comprehensive 16-week curriculum is meticulously designed to equip AI engineers with both theoretical knowledge and practical skills in Retrieval-Augmented Generation techniques (RAG) and AI Search. By engaging in hands-on projects and programming sessions, participants will not only understand the underlying concepts but also gain the experience necessary to implement and optimize RAG systems and AI search in real-world applications.

Emphasis on Practical Value: Each day’s agenda is structured to bridge the gap between theory and practice. Engineers will leave the course with a portfolio of projects, a deep understanding of advanced RAG methodologies and AI search, and the confidence to tackle complex challenges in the field of AI.

Course Topics

The following key topics will be covered:

• What is RAG: This topic provides an introduction to the fundamentals and significance of Retrieval Augmented Generation. Participants will learn how RAG combines retrieval mechanisms with generative models to enhance the quality and relevance of generated outputs.
• Evolution of RAG Techniques: Exploring the advancements from baseline RAG methods to more complex and efficient techniques. This includes a historical overview and the technological breakthroughs that have shaped the current landscape of RAG.
• Overview of RAG Solutions: A comprehensive overview of various RAG solutions available in the industry. This topic covers the different tools, libraries, and frameworks that support RAG implementations and their respective use cases.
• LlamaIndex/LangChain: Practical applications using LlamaIndex and LangChain for RAG implementation. Participants will engage with these frameworks to understand their architecture and how to utilize them effectively in real-world scenarios.
• Data Ingestion - Unstructured: Techniques for ingesting and processing unstructured data for RAG systems. This includes methods for cleaning, organizing, and preparing data to ensure it is suitable for retrieval and generation tasks.
• Re-ranking Strategies: Advanced strategies for re-ranking retrieved documents to improve accuracy. Participants will learn about various re-ranking algorithms and how to implement them to enhance the performance of RAG systems.
• Text Embedding Models: Utilizing text embedding models to enhance retrieval and generation processes. This topic covers different embedding techniques and their applications in creating more effective RAG systems.
• RAG Evaluation - RAGAS: Methods for evaluating RAG systems using the RAGAS framework. Participants will learn how to assess the performance of their RAG models and identify areas for improvement.
• GraphRAG: Implementing RAG using graph-based approaches for improved performance. This topic explores the integration of graph theory into RAG, providing new ways to handle complex data structures.
• Hybrid Search and its Necessity: Understanding the role and implementation of hybrid search techniques. Participants will learn how to combine different search methods to create more robust and accurate RAG systems.
• Sparse Neural Retriever: ColBERTv2: Leveraging ColBERTv2 for efficient sparse neural retrieval. This topic covers the architecture and use cases of ColBERTv2, a state-of-the-art sparse retriever model.
• Sparse Neural Retriever: XTR: Advanced techniques using XTR for sparse neural retrieval. Participants will explore the capabilities and implementation of XTR in enhancing RAG performance.
• RAG Strategies: Query Transformation: Transforming queries to enhance RAG performance. This includes techniques for modifying and optimizing queries to improve retrieval accuracy and relevance.
• Sparse Neural Retriever: SPLADE: Utilizing SPLADE for sparse neural retrieval and its applications. This topic covers the architecture and benefits of SPLADE in creating efficient RAG systems.
• Multimodal Embedding Models: Integrating multimodal embedding models for comprehensive data representation. Participants will learn how to handle and integrate different data types (text, images, audio) in RAG systems.
• Multimodal Data Augmentation/Feature Engineering: Techniques for augmenting and engineering features from multimodal data. This includes methods for creating richer and more informative datasets to improve RAG performance.
• Vector Database Retrieval (Qdrant): Implementing vector database retrieval using Qdrant. This topic covers the architecture and use cases of Qdrant, a powerful vector database system.
• Optional: Chatbot Frontend: Developing chatbot frontends using tools like Streamlit, Chainlit, Dash, and Reflex. Participants will learn how to create user-friendly interfaces for their RAG applications.

By the end of this course, participants will have acquired a deep understanding of advanced RAG techniques and will be equipped to apply these methods to develop and optimize high-performance AI applications in an enterprise context. Join us to enhance your proficiency and stay at the forefront of AI technology.