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Benefits of Retrieval-Augmented Generation (RAG)

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Generative AI Architecture and Application Development

Course 3 of 4 in the Learn Generative AI with LLMs Specialization

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Welcome to the 'Generative AI Architecture and Application Development' course, your gateway to mastering the advanced landscape of Generative AI and their transformative applications across industries. In this immersive course, participants will journey through the comprehensive world of LLMs, gaining insights into their foundational architecture, training methodologies, and the spectrum of applications they empower. By the end of this course, you will be equipped with the knowledge to: - Grasp the architectural nuances and training intricacies of Large Language Models, setting a solid foundation for understanding their capabilities and limitations. - Apply LLMs to a variety of tasks including search, prediction, and content generation, showcasing the versatility and power of generative AI in solving complex challenges. - Leverage the LangChain library to streamline the development of LLM applications, enhancing efficiency and innovation in your projects. - Explore advanced data interaction techniques using Retrieval-Augmented Generation (RAG), enriching the functionality and intelligence of LLM outputs. - Critically assess LLM performance, employing robust evaluation strategies to ensure your AI solutions are both effective and ethically aligned. This course is designed for a wide audience, from AI enthusiasts and software developers to data scientists and technology strategists seeking to deepen their expertise in generative AI and LLMs. Whether you are new to the field or looking to expand your knowledge, this course offers a structured path to enhancing your proficiency in leveraging LLMs for innovative solutions. A basic understanding of artificial intelligence concepts and familiarity with programming concepts are beneficial but not mandatory to complete this course. Embark on this educational journey to unlock the full potential of Large Language Models and Generative AI, propelling your professional growth and positioning you at the forefront of AI innovation.

Skills You'll Learn

Artificial Intelligence and Machine Learning (AI/ML), Prompt Engineering, Performance Tuning, Data Ethics, Natural Language Processing, Generative AI, Data Security, Personally Identifiable Information, Large Language Modeling, Information Privacy, Application Development

From the lesson

Interacting with Data Using LangChain and RAG

Interacting with Data Using LangChain and RAG provides learners with a comprehensive exploration of Retrieval-Augmented Generation (RAG) models and their integration with LangChain. Through instructional videos, practical assignments, and discussions, participants gain a deep understanding of RAG fundamentals, document loading, vector stores, retrieval techniques, and building RAG models. Emphasizing both theoretical understanding and practical skills development, the module equips learners with the knowledge and tools necessary to effectively interact with data using LangChain and RAG, empowering them to build sophisticated models for tasks such as question answering and document retrieval.

Understanding Retrieval-Augmented Generation (RAG)5:11

Usage of Retrieval-Augmented Generation (RAG)2:21

Working of Retrieval-Augmented Generation (RAG)4:07

Benefits of Retrieval-Augmented Generation (RAG)5:13

Document Loading and Splitting5:57

Working of LangChain5:13

Working of LangChain in Continuation2:47

Benefits of Document Loading and Splitting2:31

Vector Stores and Embeddings6:09

Types of Vector Stores3:17

Working of Vectors6:52

Technical Procedure Behind Retrieval4:06

Taught By

Edureka

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