LLM Prompt Engineering For Developers: The Art and Science of Unlocking LLMs' True Potential
El Amri, Aymen
- 出版商: Independently Published
- 出版日期: 2023-09-01
- 售價: $1,290
- 貴賓價: 9.5 折 $1,226
- 語言: 英文
- 頁數: 324
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9798859940714
- ISBN-13: 9798859940714
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相關分類:
LangChain
海外代購書籍(需單獨結帳)
商品描述
A practical approach to Prompt Engineering for developers. Dive into the world of Prompt Engineering agility, optimizing your prompts for dynamic LLM interactions. Learn with hands-on examples from the real world and elevate your developer experience with LLMs. Discover how the right prompts can revolutionize your interactions with LLMs.
In "LLM Prompt Engineering For Developers," we take an exhaustive 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 and GPT-4) 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, and Perplexity, and techniques like ReAct and General Knowledge Prompting, providing you with a comprehensive understanding of the domain.
This guide is designed to be hands-on, offering practical insights and exercises. 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.
商品描述(中文翻譯)
「對於 Prompt Engineering 的如此全面的觀點。很難找到一本這樣質量和深度的書籍來涵蓋這個非常新興的領域。」— MR G STEWART（亞馬遜評論）「有價值且易讀。這是一本友好但嚴謹的 Prompt Engineering 指南。書中充滿了清晰的解釋和易於跟隨的 Python 代碼。我讀這本書是為了準備開發一個專門的聊天機器人。現在我感覺準備得好多了，並且學到了許多我迫不及待想要嘗試的東西！」— Iver（亞馬遜評論）
這是一本針對開發者的實用 Prompt Engineering 方法。深入探索 Prompt Engineering 的敏捷性，優化您的提示以進行動態 LLM 互動。通過現實世界的實例學習，提升您與 LLM 的開發者體驗。發現正確的提示如何徹底改變您與 LLM 的互動。
在《LLM Prompt Engineering For Developers》中，我們將深入探討 LLM 的世界以及為其設計有效提示的藝術。
本指南首先奠定基礎，探索自然語言處理（NLP）從早期到今天我們所互動的複雜 LLM 的演變。您將深入了解 GPT 模型等模型的複雜性，理解其架構、能力和細微差別。
隨著進展，本指南強調有效的提示工程及其最佳實踐的重要性。雖然像 ChatGPT（GPT-3.5 和 GPT-4）這樣的 LLM 功能強大，但只有在有效溝通時才能實現其全部潛力。這就是提示工程發揮作用的地方。這不僅僅是向模型提出問題，而是關於措辭、上下文和理解模型邏輯。
通過專門針對 Azure Prompt Flow、LangChain 和其他工具的章節，您將獲得實際經驗，學習如何設計、測試、評分和優化提示。我們還將探討像Few-shot Learning、Chain of Thought 和 Perplexity，以及 ReAct 和 General Knowledge Prompting等進階概念和技術，為您提供該領域的全面理解。
本指南旨在提供實踐，提供實用的見解和練習。隨著進展，您將熟悉幾個工具：
- openai Python library：您將深入了解 OpenAI 的 LLM，學習如何互動和微調模型，以實現針對特定需求的精確輸出。
- promptfoo：您將掌握設計有效提示的藝術。在整個指南中，我們將使用 promptfoo 來測試和評分提示，確保它們針對所需結果進行優化。
- LangChain：您將探索 LangChain 框架，提升 LLM 驅動的應用程序。您將深入了解提示工程師如何利用這個工具的力量來測試和構建有效的提示。
- betterprompt：在部署之前，測試是至關重要的。使用 betterprompt，您將確保 LLM 提示為現實場景做好準備，並根據需要進行調整。
- Azure Prompt Flow：您將體驗 Azure 工具的視覺界面，簡化基於 LLM 的 AI 開發。您將設計可執行的流程，整合 LLM、提示和 Python 工具，確保對提示藝術的全面理解。
- 還有更多！
擁有這些工具，您將為設計強大且有效的提示做好充分準備。實踐練習將幫助鞏固您的理解。在整個過程中，您將積極參與，最終不僅會欣賞提示工程的力量，還將擁有有效實施的技能。
