What you'll Learn
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Explore the fundamentals of RAG technology by mastering innovative data ingestion, advanced text embedding, and efficient retrieval techniques to design robust pipelines.
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Deepen your expertise with state-of-the-art reranking methods and similarity search strategies while implementing both open-source and LLM-based evaluation techniques.
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Elevate your skills with advanced RAG architectures tailored for complex applications, focusing on scaling and optimizing systems.
Who Should Enroll?
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Professionals: AI professionals aiming to master advanced RAG fundamentals and system design.
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Aspiring Students: Ideal for AI enthusiasts, computer science students, and aspiring developers eager to build practical, end-to-end RAG systems and evaluation skills.
About the Instructor
Nikhil Pentapalli, Senior Machine Learning Engineer - Adobe
Nikhil is a Senior Machine Learning Engineer at Adobe, specializing in LLMs, NLP, RAG, and document intelligence. He builds AI-powered Q&A systems and generative AI evaluation metrics. As an AI Advisor at CascadeClarity.ai, he develops AI-driven solutions that turn data into insights. With a Master’s in Computer Science from the University at Buffalo, he has expertise in deep learning, OCR, and AWS solutions. Previously, he optimized AI automation at Chubb and Jio Platforms, reducing costs and improving accuracy.
FAQ's
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What is RAG?
Retrieval-Augmented Generation (RAG) is an approach that integrates a retrieval mechanism with a generative model to enhance the quality and accuracy of generated content. It works by first retrieving relevant information from a large corpus or database, then using that data to inform and improve the output of the generative model.
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What key topics will be covered?
The course covers innovative data ingestion, advanced text embedding, robust retrieval methods, and the construction and optimization of RAG pipelines.
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Will I receive a certificate upon completing the course?
Yes, the course provides a certification upon completion.
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What challenges does RAG address?
RAG tackles issues like misinformation and context loss by ensuring outputs are supported by up-to-date, retrieved data.
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How does RAG compare to retrieval-only or generation-only models?
RAG offers the advantage of grounding responses in factual data, reducing errors common in pure generative models and providing more context than retrieval-only approaches.