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Advanced LLM Design: Retrieval, Context, and Prompts

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Advanced LLM Design: Retrieval, Context, and Prompts

This course is part of LLMs in Enterprise Specialization

Instructor: Packt - Course Instructors

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Apply design patterns to integrate LLMs into enterprise applications for efficiency and scalability
Overcome challenges in scaling and deploying LLMs in complex environments
Use fine-tuning techniques and RAG approaches to enhance LLM efficiency

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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This course is part of the LLMs in Enterprise Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in 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.

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.

What's included

1 video8 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

8 readingsTotal 49 minutes

Introduction5 minutes
Overcoming Context Limitations with Retrieval7 minutes
Foundations of Retrieval Mechanisms5 minutes
Approximate Nearest Neighbor Search6 minutes
Enterprise-specific Evaluation Considerations6 minutes
Domain Adaptation of Embeddings6 minutes
System Architecture Overview6 minutes
Combining Multiple Retrieval Methods8 minutes

1 assignmentTotal 16 minutes

Exploring Retrieval-Augmented Generation Techniques16 minutes

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.

What's included

1 video6 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

6 readingsTotal 41 minutes

Introduction5 minutes
RAGs in Enterprise6 minutes
Technical and Optimization Challenges6 minutes
Implementing Hybrid Search5 minutes
The Complexity of Prompt Engineering10 minutes
Use Case Using RAG to Enhance Information Retrieval9 minutes

1 assignmentTotal 16 minutes

Customizing Contextual Language Models16 minutes

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.

What's included

1 video8 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

8 readingsTotal 45 minutes

Introduction4 minutes
The Power of Prompt Engineering6 minutes
Case Study Healthcare Information Management6 minutes
Understanding the Science Behind Prompt Engineering7 minutes
Information Processing in LLMs6 minutes
Real-world Application Enterprise Policy Analysis5 minutes
Continuous Improvement5 minutes
Managing and Mitigating Hallucinations in LLMs6 minutes

1 assignmentTotal 16 minutes

Mastering Effective Prompt Design for Enterprise AI16 minutes

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.

What's included

1 video9 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

9 readingsTotal 43 minutes

Introduction4 minutes
Response Variability and Robustness5 minutes
Enterprise-specific Challenges in Evaluating LLM Applications6 minutes
Real-world Examples of Enterprise LLM Applications4 minutes
Recommendations for Approaching LLM Evaluation6 minutes
Example Code for Bridging the Gap Between Evaluation and Improvement4 minutes
Bridging Evaluation and Metrics A Data-Driven Approach6 minutes
Human Evaluation Metrics4 minutes
Metrics for Evaluating LLM Performance4 minutes

1 assignmentTotal 16 minutes

Challenges in Assessing AI Applications16 minutes

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Instructor

Packt - Course Instructors

Packt

1,888 Courses517,752 learners

Offered by

Packt

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.