Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off

One annual plan covers every course and certificate on Coursera. 40% off for a limited time.

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.

Syllabus

Retrieval-Augmented Generation Pattern

This module introduces the retrieval-augmented generation (RAG) pattern, focusing on how language models can be enhanced by integrating real-time retrieval from external data sources. Learners will explore retrieval mechanisms, approximate nearest neighbor search, domain adaptation, and hybrid retrieval strategies, with a special emphasis on enterprise-specific considerations. By the end, you'll understand how to architect and evaluate RAG systems for specialized information needs.

Customizing Contextual LLMs

This module explores advanced strategies for tailoring large language models (LLMs) to enterprise needs, focusing on retrieval-augmented generation (RAG), hybrid search techniques, and prompt engineering. Learners will examine technical challenges, optimization considerations, and practical use cases for enhancing information retrieval and accuracy in AI systems.

The Art of Prompt Engineering for Enterprise LLMs

This module explores the principles and best practices of prompt engineering for large language models in enterprise environments. Learners will discover how to craft effective prompts, understand LLM information processing, and implement strategies for continuous improvement and error mitigation. Real-world case studies illustrate the impact of prompt design on model performance and reliability.

Enterprise Challenges in Evaluating LLM Applications

This module examines the unique challenges enterprises face when evaluating large language model (LLM) applications, including response variability, robustness, and the need for tailored evaluation metrics. Learners will explore real-world case studies, compare automated and human evaluation methods, and gain practical skills for bridging evaluation insights with model improvement. By the end, participants will be equipped to assess and enhance LLM performance in complex enterprise environments.