فهرست مطالب

• Cover
• Copyright
• Contributors
• Table of Contents
• Preface
• Chapter 1: Introduction to Large Language Models
• What are large foundation models and LLMs?
• Most popular LLM transformers-based architectures
• Training and evaluating LLMs
• Base models versus customized models
• Summary
• References
• Chapter 2: LLMs for AI-Powered Applications
• How LLMs are changing software development
• The copilot system
• Introducing AI orchestrators to embed LLMs into applications
• Summary
• References
• Chapter 3: Choosing an LLM for Your Application
• The most promising LLMs in the market
• Beyond language models
• A decision framework to pick the right LLM
• Summary
• References
• Chapter 4: Prompt Engineering
• Technical requirements
• What is prompt engineering?
• Principles of prompt engineering
• Advanced techniques
• Summary
• References
• Chapter 5: Embedding LLMs within Your Applications
• Technical requirements
• A brief note about LangChain
• Getting started with LangChain
• Working with LLMs via the Hugging Face Hub
• Summary
• References
• Chapter 6: Building Conversational Applications
• Technical requirements
• Getting started with conversational applications
• Developing the front-end with Streamlit
• Summary
• References
• Chapter 7: Search and Recommendation Engines with LLMs
• Technical requirements
• Introduction to recommendation systems
• Existing recommendation systems
• How LLMs are changing recommendation systems
• Implementing an LLM-powered recommendation system
• Developing the front-end with Streamlit
• Summary
• References
• Chapter 8: Using LLMs with Structured Data
• Technical requirements
• What is structured data?
• Getting started with relational databases
• Implementing the DBCopilot with LangChain
• Developing the front-end with Streamlit
• Summary
• References
• Chapter 9: Working with Code
• Technical requirements
• Choosing the right LLM for code
• Code understanding and generation
• Act as an algorithm
• Leveraging Code Interpreter
• Summary
• References
• Chapter 10: Building Multimodal Applications with LLMs
• Technical requirements
• Why multimodality?
• Building a multimodal agent with LangChain
• Option 1: Using an out-of-the-box toolkit for Azure AI Services
• Option 2: Combining single tools into one agent
• Option 3: Hard-coded approach with a sequential chain
• Comparing the three options
• Developing the front-end with Streamlit
• Summary
• References
• Chapter 11: Fine-Tuning Large Language Models
• Technical requirements
• What is fine-tuning?
• When is fine-tuning necessary?
• Getting started with fine-tuning
• Summary
• References
• Chapter 12: Responsible AI
• What is Responsible AI and why do we need it?
• Responsible AI architecture
• Regulations surrounding Responsible AI
• Summary
• References
• Chapter 13: Emerging Trends and Innovations
• The latest trends in language models and generative AI
• Companies embracing generative AI
• Summary
• References
• Packt Page
• Other Books You May Enjoy
• Index