In "LLM Prompt Engineering For Developers," we take a comprehensive journey into the world of LLMs and the art of crafting effective prompts for them.

The guide starts by laying the foundation, exploring the evolution of Natural Language Processing (NLP) from its early days to the sophisticated LLMs we interact with today. You will dive deep into the complexities of models such as GPT models, understanding their architecture, capabilities, and nuances.

As we progress, this guide emphasizes the importance of effective prompt engineering and its best practices. While LLMs like ChatGPT (gpt-3.5) are powerful, their full potential is only realized when they are communicated with effectively. This is where prompt engineering comes into play. It's not simply about asking the model a question; it's about phrasing, context, and understanding the model's logic.

Through chapters dedicated to Azure Prompt Flow, LangChain, and other tools, you'll gain hands-on experience in crafting, testing, scoring and optimizing prompts. We'll also explore advanced concepts like Few-shot Learning, Chain of Thought, Perplexity and techniques like ReAct and General Knowledge Prompting, equipping you with a comprehensive understanding of the domain.

This guide is designed to be hands-on, offering practical insights and exercises. In fact, as you progress, you'll familiarize yourself with several tools:

- openai Python library: You will dive into the core of OpenAI's LLMs and learn how to interact and fine-tune models to achieve precise outputs tailored to specific needs.

- promptfoo: You will master the art of crafting effective prompts. Throughout the guide, we'll use promptfoo to test and score prompts, ensuring they're optimized for desired outcomes.

- LangChain: You’ll explore the LangChain framework, which elevates LLM-powered applications. You’ll dive into understanding how a prompt engineer can leverage the power of this tool to test and build effective prompts.

- betterprompt: Before deploying, it's essential to test. With betterprompt, you'll ensure the LLM prompts are ready for real-world scenarios, refining them as needed.

- Azure Prompt Flow: You will experience the visual interface of Azure's tool, streamlining LLM-based AI development. You'll design executable flows, integrating LLMs, prompts, and Python tools, ensuring a holistic understanding of the art of prompting.

- And more!

With these tools in your toolkit, you will be well-prepared to craft powerful and effective prompts. The hands-on exercises will help solidify your understanding. Throughout the process, you'll be actively engaged and by the end, not only will you appreciate the power of prompt engineering, but you'll also possess the skills to implement it effectively.

The knowledge you'll acquire from this guide will be applicable to the current families of GPT models (GPT-3, GPT-3.5, GPT-4, etc.) and will likely also be relevant to GPT-5, should it ever be released.

OpenAI provides APIs (Application Programming Interfaces) to access their AI. The goal of an API is to abstract the underlying models by creating a universal interface for all versions, allowing users to use GPT regardless of its version.

This guide aims to provide a comprehensive, step-by-step tutorial on how to utilize GPT-3.5 and GPT-4 in your projects via this API. It also covers other models, such as Whisper and Text-to-Speech.

If you're developing a chatbot, an AI assistant, or a web application that utilizes AI-generated data, this guide will assist you in achieving your objectives.

If you have a basic understanding of the Python programming language and are willing to learn a few additional techniques, such as using Pandas Dataframes and some NLP methods, you possess all the necessary tools to start building intelligent systems with OpenAI tools.

Rest assured, you don't need to be a data scientist, machine learning engineer, or AI expert to comprehend and implement the concepts, techniques, and tutorials presented in this guide. The explanations provided are straightforward and easy to understand, featuring simple Python code, examples, and hands-on exercises.

This guide emphasizes practical, hands-on learning and is designed to assist readers in building real-world applications. It is example-driven and provides numerous practical examples to help readers understand the concepts and apply them to real-life scenarios to solve real-world problems.

By the end of your learning journey, you will have developed applications such as:

• Fine-tuned, domain-specific chatbots.
• An intelligent conversational system with memory and context.
• A semantic modern search engine using RAG and other techniques.
• An intelligent coffee recommendation system based on your taste.
• A chatbot assistant to assist with Linux commands
• A fine-tuned news category prediction system.
• An AI-to-AI autonomous discussion system to simulate human-like conversations or solve problems
• An AI-based mental health coach trained on a large dataset of mental health conversations
• and more!

By reading this guide and following the examples, you will be able to:

• Understand the different models available, and how and when to use each one.
• Generate human-like text for various purposes, such as answering questions, creating content, and other creative uses.
• Control the creativity of GPT models and adopt the best practices to generate high-quality text.
• Transform and edit the text to perform translation, formatting, and other useful tasks.
• Optimize the performance of GPT models using various parameters and options such as max_tokens, temperature, top_p, n, stream, logprobs, stop, presence_penalty, frequency_penalty, best_of, and others.
• Stem, lemmatize and reduce your costs when using the API.
• Understand Context Stuffing, chaining, and practice prompt engineering.
• Implement a chatbot with memory and context.
• Create prediction algorithms and zero-shot techniques and evaluate their accuracy.
• Understand, practice, and improve few-shot learning.
• Understand fine-tuning and leverage its power to create your own fine-tuned models.
• Understand and use fine-tuning best practices
• Practice training and classification techniques using GPT.
• Understand embedding and how companies such as Tesla and Notion are using it.
• Understand and implement semantic search, RAG, and other advanced tools and concepts.
• Integrate a Vector Database (e.g.: Weaviate) with your intelligent systems.

The primary purpose of this book is to provide the average person with a comprehensive, 360-degree view of the Generative AI landscape.

It simplifies the material in a way that is suitable for both beginners and experts, enabling readers to understand everything they need to know about the topic and prepare for the future.

From foundational to cutting-edge, the book demystifies complex AI concepts in an easily understandable manner.

Even without a solid background in AI, readers will be able to grasp the most complex concepts.

The book answers many questions, including:

General knowledge

• What is Generative AI?
• How has AI evolved from the 1950s to the present day?
• What are the most common foundational Generative AI models?
• What are the differences between ChatGPT, Google Gemini, Claude, Mistral, LLaMA, Amazon Titan, Alibaba Tongyi Tingwu, and other Generative AI models?
• What are the most common applications of Generative AI?

Our Economy

• In what ways is the US leading the global AI industry, and why is China catching up very quickly?
• How are the UK, Canada, Germany, France, Japan, South Korea, and other countries investing in AI?
• What are the most common AI strategies and policies worldwide?
• What are the current trends in Generative AI globally?
• What is the relationship between AI, economy, and geopolitics?
• How is the semiconductor industry shaping the future of Generative AI?

The Workforce

• In what ways does AI automate tasks and transform jobs?
• Will AI take over your job?
• What are the predicted impacts of Generative AI on global GDP and personal income?
• How is Generative AI used in healthcare, finance, marketing, education, retail, manufacturing, and other industries?
• What are the most common myths about AI?

The Applications

• What are the leading applications of Generative AI that you can start using today?
• In what ways do AI-assisted platforms facilitate content creation, including writing, image generation, voice synthesis, and music composition, thereby enhancing creativity and productivity?

The Technology

• How are generative AI models created?
• What is the difference between "symbolic AI" and "connectionist AI"?
• How are AI, machine learning, deep learning, and generative AI related?
• What is supervised learning, unsupervised learning, and reinforcement learning?
• What is autonomous generative AI and how is it different?

Your Productivity

• How are costs for Generative AI services, like ChatGPT, Google Gemini, Claude, and similar platforms, calculated for regular users and developers?
• What is "tokenization," and how is it used in Generative AI?
• How can you increase your productivity using Prompt Engineering?
• What are the most advanced Prompt Engineering techniques?

Ethics, Regulation, and Security

• How can AI wipe out humanity?
• How is the Dead Internet Theory related to Generative AI?
• Why is the Godfather of Deep Learning worried about the future?
• How do figures like Elon Musk, Geoffrey Hinton, and Chomsky view the potential risks of Generative AI? Are their concerns valid?
• What are the limitations and criticisms of current Generative AI technologies in terms of understanding and intelligence?
• How are the United States, China, the EU, and the UK approaching AI regulation?
• How is Generative AI harming the environment?
• Should we be optimistic or pessimistic about the future of Generative AI?

Filled with interesting facts, statistics, demonstrations, criticisms, and answers to a multitude of questions, the book is a must-read for anyone interested in their future!

The Augmented Developer is a comprehensive guide to AI-assisted coding. It covers everything from the use of AI in code completion, generation, refactoring, security, and bug detection, to the creation of custom model chains and AI assistants.

The guide dives into advanced prompt engineering techniques and best practices used to maximize the potential of AI in coding. It also explores the ethical and technical implications of using generative AI in coding. Moreover, it looks into the future impacts of AI in coding and its potential to change the developer role as we know it.

Across the chapters of The Augmented Developer, you will:

• Discover the Fusion of Pair Programming and AI: Learn how AI-assisted coding is reshaping the dynamics of pair programming and remote work. Understand how these tools not only support but also enhance the overall coding experience.
• Unpack GitHub Copilot: Dive into the specifics of GitHub Copilot, from its foundational AI training to its real-world application in coding. This section breaks down its features, pricing, and setup, making it practical for you to start using it in your projects right away.
• Learn How to Use GitHub Copilot Effectively: Master the art of using GitHub Copilot to boost your productivity. From writing code, debugging, refactoring, security hardening, unit testing, documentation, code reviews, pull requests, context-aware prompting, and more. You'll gain a deep understanding of how to leverage GitHub Copilot to its full potential.
• Explore a Spectrum of AI Coding Assistants and Agents: Get acquainted with the broad landscape of AI coding tools available today: Tabnine, DeepCode, fauxpilot, privy, aider, Devin, SWE-Agent, Codeium, Tabby, CodeGeeX, Amazon CodeWhisperer, AskCodi, Blackbox AI, Bito, and many more.. We'll compare their features, assess their impacts, and discuss their potential applications.
• Master Prompt Engineering for Developer Productivity: Learn how to write better prompts for GitHub Copilot, ChatGPT and AI coding assistants. This section will guide you through the process of crafting prompts that yield the best results. From Few-Shot Learning, Chain of Thoughts, Self Consistency, Tree of Thoughts, Autonomous Agents, General Knowledge Prompting, and more, you'll gain a deep understanding of how to maximize the potential of AI in coding using the right prompts.
• Understand the Best Practices of Prompt Engineering: Acknowledge the best practices of prompt engineering and how you can apply them with the techniques you've learned. This section will provide you with a clear roadmap to follow when writing prompts for AI coding assistants.
• Build Your In-house Developer Productivity Tools: Learn how to use tools like OpenAI APIs, LangChain, Chroma, and others to code your own AI-powered developer productivity tools. This section will guide you through the process of coding your custom AI assistants, step by step.
• Understand Advanced Concepts in Generative AI: Gain a clear understanding of useful concepts such as RAG, vector databases, embeddings, tokens, and more.
• Discover Advanced LLM Orchestration Tools: Immerse yourself in the world of advanced LLM orchestration and workflow tools. From DiFy and Flowise AI to LLMStack and others, you'll receive an overview of the best tools to help you create your own LLM-based solutions in a matter of minutes.
• Understand the Ethical and Technical Implications of AI in Coding: Gain a clear perspective on these implications and answer the most pressing questions about AI in coding. What's controversial about AI in coding? Can we really trust AI to write code? Will AI replace developers? And more.

After reading this book, you'll possess the practical knowledge and tools needed to effectively use AI in coding, thereby enhancing your productivity as a developer.