This course provides a comprehensive guide to building enterprise-level Large Language Model (LLM) applications, with a focus on Retrieval-Augmented Generation (RAG). You’ll learn the intricacies of designing scalable, efficient pipelines while tackling real-world challenges like large document retrieval, query ambiguity, and generation complexities. The course covers advanced techniques for indexing, query optimization, retrieval enhancements, and generation refinements. By the end, you’ll be equipped with the knowledge to build robust, scalable, and efficient LLM applications for enterprise use.
What you'll learn
- Understand the limitations of basic RAG pipelines and how to optimize them.
- Overcome generation challenges such as hallucinations and chaotic contexts with techniques like ThoT and Chain of Note.
- Address query-related challenges like ambiguity, multi-query retrieval, and transformations.
- Enhance retrieval with hybrid search, hierarchical indexing, and sentence windowing.
- Optimize document chunking, embedding models, and retrieval methods.
- Implement access control and governance for secure enterprise LLM applications.
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Our programs are available in the most popular formats: in-person, virtual instructor-led, and self-paced training. This means that you can choose the learning style that works best for you! From the very beginning, our focus is on helping students develop a think-business-first mindset so that they can effectively apply their data science skills in a real-world context. Enrol in one of our highly-rated programs and learn the practical skills you need to succeed in the field.
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