Advanced LLM Design: Retrieval, Context, and Prompts

About this course

This course delves into advanced design patterns for large language models (LLMs), emphasizing retrieval-augmented generation, contextual customization, and prompt engineering tailored for enterprise solutions. These techniques enhance LLM performance, making it more reliable for complex business scenarios. Learners will be guided through sophisticated strategies to optimize LLMs in business contexts, such as hybrid search, retrieval-augmented generation (RAG), and advanced prompt engineering. The course focuses on the challenges of contextual adaptation and managing hallucinations, helping learners to meet enterprise-specific needs. This course uniquely blends technical theory with real-world applications, enabling professionals to refine and integrate LLMs within complex environments. Expert insights and practical frameworks empower learners to implement robust and scalable LLM solutions that drive business outcomes. This course is designed for professionals in AI, data science, and business technology who are looking to build and refine enterprise-level AI solutions. A foundational understanding of machine learning and AI is recommended. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Course objectives

• optimize LLMs for enterprise use
• address challenges in contextual adaptation
• apply sophisticated strategies for complex business problems