Prompt Engineering Advanced Strategies For Optimizing Language Models Like Chatgpt
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Prompt Engineering: Advanced Strategies for Optimizing Language Models like ChatGPT
This book takes an in-depth look at Prompt Engineering, an essential skill for getting the most out of advanced language models like ChatGPT. Through a combination of key concepts, practical strategies, and detailed examples, you'll learn how to design clear and effective prompts that optimize AI responses. From the basics of interacting with these models to advanced techniques for complex tasks, the book offers a comprehensive guide that will enable you to communicate more accurately and efficiently with artificial intelligence. In addition, the content covers diverse use cases in areas such as productivity, creativity, problem solving, and much more. It also includes an extensive list of optimized prompts for everyday and specific tasks, providing the reader with practical tools that can be implemented immediately. This book is ideal for anyone who wants to take full advantage of ChatGPT's capabilities, regardless of their level of prior AI experience.
Quick Start Guide to Large Language Models
Author: Sinan Ozdemir
language: en
Publisher: Addison-Wesley Professional
Release Date: 2024-09-26
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Generative AI Courseware
The course is mainly practical (applying generative AI/prompt-engineering), in action activities where you make prompts and learn best practices on Generative AI, while doing. Gain a comprehensive understanding of Generative AI and its capabilities Develop critical thinking skills to evaluate the potential of Generative AI in different business use cases Understanding of the architecture behind the model Master the skill of prompt engineering for effective interaction with Generative AI Learn how to integrate Generative AI into existing workflows Understand the limitations and risks associated with Generative AI Individuals who need a basic understanding Generative AI Professionals who want to learn the potential and pitfalls of Generative AI and how to deal with it Business Analyst. The course is mainly practical, with interactive activities where you make prompts and learn by doing. You will also get feedback and guidance from experienced instructors and peers. By the end of the course, you will be able to: Gain a comprehensive understanding of Generative AI and its capabilities Develop critical thinking skills to evaluate the potential of Generative AI in different business use cases Master the skill of prompt engineering for effective interaction with Generative AI Learn how to integrate Generative AI into existing workflows Understand the limitations and risks associated with Generative AI The course is suitable for anyone who needs a basic understanding of Generative AI, such as: Professionals who want to learn the potential and pitfalls of Generative AI and how to deal with them Business analysts, marketers, content creators, educators, researchers, and other roles that can benefit from using Generative AI Students who want to explore the field of Generative AI and its applications The course prepares you for the EDF Certified Generative AI Ambassador exam, which is a certification program offered by the Effective Data Foundation (EDF) and Van Haren Certify. By passing this exam, you will become a certified Generative AI Ambassador and gain a competitive edge in your field.