Programming Large Language Models with Azure Open AI: Conversational programming and prompt engineering with LLMs (Developer Reference)

✍ Scribed by Francesco Esposito

• Publisher: Microsoft Press
• Year: 2024
• Tongue: English
• Leaves: 257
• Edition: 1
• Category: Library

✦ Synopsis

Use LLMs to build better business software applications

Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programming―with specific techniques for patterns and frameworks―unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input.

Artificial Intelligence expert Francesco Esposito helps you:

• Understand the history of large language models and conversational programming
• Apply prompting as a new way of coding
• Learn core prompting techniques and fundamental use-cases
• Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines
• Use natural language in code to define workflows and orchestrate existing APIs
• Master external LLM frameworks
• Evaluate responsible AI security, privacy, and accuracy concerns
• Explore the AI regulatory landscape
• Build and implement a personal assistant
• Apply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge base
• Construct a conversational user interface

For IT Professionals and Consultants

• For software professionals, architects, lead developers, programmers, and Machine Learning enthusiasts
• For anyone else interested in natural language processing or real-world
  applications of human-like language in software

✦ Table of Contents

Cover
Title Page
Copyright Page
Contents at a Glance
Contents
Acknowledgments
Introduction
Chapter 1 The genesis and an analysis of large language models
LLMs at a glance
History of LLMs
Functioning basics
Business use cases
Facts of conversational programming
The emerging power of natural language
LLM topology
Future perspective
Summary
Chapter 2 Core prompt learning techniques
What is prompt engineering?
Prompts at a glance
Alternative ways to alter output
Setting up for code execution
Basic techniques
Zero-shot scenarios
Few-shot scenarios
Chain-of-thought scenarios
Fundamental use cases
Chatbots
Translating
LLM limitations
Summary
Chapter 3 Engineering advanced learning prompts
What’s beyond prompt engineering?
Combining pieces
Fine-tuning
Function calling
Homemade-style
OpenAI-style
Talking to (separated) data
Connecting data to LLMs
Embeddings
Vector store
Retrieval augmented generation
Summary
Chapter 4 Mastering language frameworks
The need for an orchestrator
Cross-framework concepts
Points to consider
LangChain
Models, prompt templates, and chains
Agents
Data connection
Microsoft Semantic Kernel
Plug-ins
Data and planners
Microsoft Guidance
Configuration
Main features
Summary
Chapter 5 Security, privacy, and accuracy concerns
Overview
Responsible AI
Red teaming
Abuse and content filtering
Hallucination and performances
Bias and fairness
Security and privacy
Security
Privacy
Evaluation and content filtering
Evaluation
Content filtering
Summary
Chapter 6 Building a personal assistant
Overview of the chatbot web application
Scope
Tech stack
The project
Setting up the LLM
Setting up the project
Integrating the LLM
Possible extensions
Summary
Chapter 7 Chat with your data
Overview
Scope
Tech stack
What is Streamlit?
A brief introduction to Streamlit
Main UI features
Pros and cons in production
The project
Setting up the project and base UI
Data preparation
LLM integration
Progressing further
Retrieval augmented generation versus fine-tuning
Possible extensions
Summary
Chapter 8 Conversational UI
Overview
Scope
Tech stack
The project
Minimal API setup
OpenAPI
LLM integration
Possible extensions
Summary
Appendix: Inner functioning of LLMs
Index
A
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D
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