Title Page
Copyright Page
Table of Contents
Introduction
About This Book
Icons Used in This Book
Beyond the Book
Chapter 1 Introducing Gen AI and the Role of Data
The Historical Context of Gen AI
Introducing LLMs and foundation models
Transforming the AI landscape
Accelerating AI functions
The Role of Data in AI Projects
Explaining the Importance of Generative AI to the Enterprise
Pretrained models
Security versus ease of use
Managing Gen AI Projects with a Cloud Data Platform
Chapter 2 Understanding Large Language Models
Categorizing LLMs
Defining general-purpose LLMs
Using task-specific and domain-specific LLMs
Reviewing the Technology Behind LLMs
Introducing key terms and concepts
Explaining the importance of vector embeddings
Identifying developer tools and frameworks
Enforcing data governance and security
Extending governance for all data types
Chapter 3 LLM App Project Lifecycle
Defining the Use Case and Scope
Selecting the right LLM
Comparing small and large language models
Adapting LLMs to Your Use Case
Engineering prompts
Learning from context
Augmenting text retrieval
Fine-tuning language models
Reinforcement learning
Using a vector database
Implementing LLM Applications
Deploying apps into containers
Allocating specialized hardware
Integrating apps and data
Chapter 4 Bringing LLM Apps intoΒ Production
Adapting Data Pipelines
Semantic caching
Feature injection
Context retrieval
Processing for Inference
Reducing latency
Calculating costs
Creating User Interfaces
Simplifying Development and Deployment
Orchestrating AI Agents
Chapter 5 Reviewing Security and Ethical Considerations
Reiterating the Importance of Security and Governance
Centralizing Data Governance
Alleviating Biases
Acknowledging Open-Source Risks
Contending with Hallucinations
Observing Copyright Laws
Chapter 6 Five Steps to GenerativeΒ AI
Identify Business Problems
Select a Data Platform
Build a Data Foundation
Create a Culture of Collaboration
Measure, Learn, Celebrate
EULA