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About the book: A Bird’s Eye View on Marketing Management ISBN No.: 978-93-5747-743-7 E-ISBN No.: 978-93-5747-566-2 Author: Dr. (H.C) Rahul Joshi Govt. Copyright Reg. No.: L - 143932/2024 This book is basically a guide to all those who are marketing enthusiasts or those who wish to understand the concept of Marketing and its related branches like Sales and Distribution Management, Product Management, Brand Management, Service Marketing, concepts like ROI with practical application in real-time scenario, concepts of Strategic management and Supply Chain Management. All these concepts are interrelated to each other and help the marketer in making correct and concise decisions to grow in professional life. I wrote this book by teaching all my knowledge and experience of the corporate world so that the reader gets maximum benefit in one go. This is the only reason I named this book as “A Bird’s Eye View on Marketing Management”. While writing this book I also keep this thing in mind that while reading this book, the reader must not feel monotonous that’s why I included figures, charts, and diagrams to make things more convenient to understand. Hope you enjoy the reading and have a good takeaway as learning and understanding the subject better.

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Leading CEOs, Champions such as Virat Kohli, Oprah Winfrey, Marc Benioff, Arianna Huffington practice meditation. I find it as a game changer from personal experiences. Consider exploring this form. Deep gratitude. Do Serve Love, Nitin Dua

"Prompt Engineering Best Practices for Instruction-Tuned LLM" is a comprehensive guide designed to equip readers with the essential knowledge and tools to master the fine-tuning and prompt engineering of large language models (LLMs). The book covers everything from foundational concepts to advanced applications, making it an invaluable resource for anyone interested in leveraging the full potential of instruction-tuned models. The first part, Introduction to LLM Instruction Fine Tuning, offers a deep dive into the core concepts behind instruction fine-tuning. As LLMs become increasingly critical in various industries, understanding how to tailor them to specific tasks is vital. This section introduces readers to the nuances of single-task versus multi-task fine-tuning, scaling these models, and evaluating their performance, culminating in a hands-on guide for applying instruction fine-tuning to summarization tasks. The second part, Prompt Engineering Guide & Best Practices, shifts focus to the art of crafting effective prompts. Prompt engineering allows users to control how LLMs respond, making it essential to ensure precision and reliability. This part covers everything from basic prompt design to more complex techniques like Chain of chain-of-thought reasoning, transforming text, and iterating on prompts for enhanced results. Each chapter is filled with actionable strategies for optimizing prompts across diverse use cases, ensuring LLMs are used to their full potential. In the final part, Building Projects with Prompt Engineering, the book moves from theory to practice, guiding readers through the development of real-world LLM-powered applications. From creating intelligent chatbots to designing end-to-end customer service systems, this section provides step-by-step instructions to turn prompt engineering concepts into functional, deployable tools. Table of Contents: Part I: Introduction to LLM Instruction Fine Tuning Overview of Instruction Fine Tuning Single Vs Multi-Task LLM Instruction Fine-Tuning Overview of Scaling Instruction-Tuned LLMs How Can We Evaluate Instruction Tuned LLM? Instruction Fine-Tuning LLM for Summarization: Step-by-Step Guide Part II: Prompt Engineering Guide & Best Practices Prompt Engineering Guidelines Iterative Prompt Development Text Summarization & Information Retrieval Textual Inference & Sentiment Analysis Text Transforming & Translation Text Expansion & Generation Chain of Thought Reasoning LLM Output Validation & Evaluation Part III: Building Projects with Prompt Engineering Building Chatbots Using Prompt Engineering Building an End-to-End Customer Service System Testing Prompt Engineering-Based LLM Applications

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