Textual Intelligence: Large Language Models and Their Real-World Applications

The book is a must-have resource for anyone looking to understand the complexities of generative AI, offering comprehensive insights into LLMs, effective training strategies, and practical applications.

Textual Intelligence: Large Language Models and Their Real-World Applications provides an overview of generative AI and its multifaceted applications, as well as the significance and potential of Large Language Models (LLMs), including GPT and LLaMA. It addresses the generative AI project lifecycle, challenges in existing data architectures, proposed use case planning and scope definition, model deployment, and application integration. Training LLMs, data requirements for effective LLM training, pre-training and fine-tuning processes, and navigating computational resources and infrastructure are also discussed. The volume delves into in-context learning and prompt engineering, offering strategies for crafting effective prompts, techniques for controlling model behavior and output quality, and best practices for prompt engineering.

Textual Intelligence: Large Language Models and Their Real-World Applications also discusses cost optimization strategies for LLM training, aligning models to human values, optimizing model architectures, the power of transfer learning and fine-tuning, instruction fine-tuning for precision, and parameter-efficient fine-tuning (PEFT) with adapters such as LoRA, QLoRA, and soft prompts, making it an essential guide for both beginners and industry veterans.

Readers will find this book:

• Explores the real-world potential of large language models;
• Introduces industry-changing AI solutions;
• Provides advanced insights on AI and its models.

Audience

Industry professionals, academics, graduate students, and researchers seeking real-world solutions using generative AI.

Meenakshi Malik, PhD is an assistant professor at BML Munjal University, India, with over 12 years of experience. She earned her computer science and engineering doctorate at Maharshi Dayanand University, Rohtak, in December 2023 and was honored by the Vice President for her exceptional PhD research. Her research interests include artificial intelligence, machine learning, deep learning, and big data.

Preeti Sharma, PhD is a faculty member at Chitkara University, Punjab, India. She is the author or co-author of over 12 publications in national and international journals and conferences. Dr. Sharma’s research interests include extensive work in blockchain and its diverse applications, as well as artificial intelligence and machine learning.

Susheela Hooda, PhD is an associate professor in the Department of Computer Science and Engineering, Chitkara University, Punjab, India. She has published over 30 technical research papers in national and international journals and conferences. Her research interests include software engineering, aspect-oriented software development, software testing, cloud computing, artificial intelligence, and machine learning.