Building LLM Powered Applications
Building LLM Powered Applications
Create intelligent apps and agents with large language models
About the Book
Building LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities.
The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio.
Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.
About the Author
Table of Contents
- Introduction to Large Language Models
- LLMs for AI-Powered Applications
- Choosing an LLM for Your Application
- Prompt Engineering
- Embedding LLMs within Your Applications
- Building Conversational Applications
- Search and Recommendation Engines with LLMs
- Using LLMs with Structured Data
- Working with Code
- Building Multimodal Applications with LLMs
- Fine-Tuning Large Language Models
- Responsible AI
- Emerging Trends and Innovations
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