Programming Large Language Models With Azure Open Ai: Conversational Programming and Prompt Engineering With Llms (Developer Reference)

✍ Scribed by Esposito, Francesco

• Publisher: Microsoft Pr
• Year: 2024
• Tongue: English
• Leaves: 661
• 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

Title Page
Copyright Page
Dedication
Contents at a Glance
Contents
Acknowledgments
Introduction
Who should read this book
Assumptions
This book might not be for you if…
Organization of this book
Downloads: notebooks and samples
Errata, updates, & book support
Stay in touch
Chapter 1. The genesis and an analysis of large language models
LLMs at a glance
Facts of conversational programming
Summary
Chapter 2. Core prompt learning techniques
What is prompt engineering?
Basic techniques
Fundamental use cases
LLM limitations
Summary
Chapter 3. Engineering advanced learning prompts
What’s beyond prompt engineering?
Function calling
Talking to (separated) data
Summary
Chapter 4. Mastering language frameworks
The need for an orchestrator
LangChain
Microsoft Semantic Kernel
Microsoft Guidance
Summary
Chapter 5. Security, privacy, and accuracy concerns
Overview
Security and privacy
Evaluation and content filtering
Summary
Chapter 6. Building a personal assistant
Overview of the chatbot web application
The project
Summary
Chapter 7. Chat with your data
Overview
What is Streamlit?
The project
Progressing further
Summary
Chapter 8. Conversational UI
Overview
The project
Summary
Appendix. Inner functioning of LLMs
The role of probability
The case of GPT
Index
Code Snippets