AI提示工程必知必会：揭秘特定场景下的提示词设计艺术 (王国平 编著) (z-library.sk, 1lib.sk, z-lib.sk)

【One-Line Pitch】 A practical, scenario-driven handbook for anyone who wants to write better prompts for large language models—covering everyday text tasks, coding, data mining, AI art, and China’s mainstream platforms (Baidu Wenxin Yiyan and Alibaba Tongyi). If you are a beginner or a working professional looking for concrete prompt recipes rather than abstract theory, this book is for you. 【Book Arc】 - **Opening (~0%–10%)**: Introduces the core idea of prompt engineering and why the *way you ask* determines the quality of AI output. Sets up a taxonomy of prompt types—question-answering, instructional, classification, suggestion, safety, and gratitude—so readers can match a prompt style to a task. - **Early (~10%–30%)**: Walks through hands-on drills for each prompt category, showing how small wording changes alter results. This section is built around practice: you are expected to try prompts, observe outputs, and iterate. - **Middle (~30%–60%)**: Moves from generic prompts to language-processing tasks—text summarization, rewriting/paraphrasing, grammar correction, and machine translation. Each task gets its own prompt patterns and troubleshooting tips. - **Late (~60%–80%)**: Extends prompt use into non-text domains: data mining and program development. Here the book shows how to frame questions for structured analysis and code generation, emphasizing clarity and context. - **Ending (~80%–100%)**: Covers AI image generation prompts, then profiles two Chinese AI platforms—Baidu Wenxin Yiyan and Alibaba Tongyi—highlighting their unique features. Closes with a market-research case study that ties all prompt skills into a realistic workflow. 【Key Takeaways】 - **Prompt type matters more than length** (Early): The book categorizes prompts into six functional types—question-answering, instruction, classification, suggestion, safety, and gratitude—and argues that choosing the right type for your goal is the first step to reliable output. This gives you a mental checklist before you type anything. - **Iteration is the core skill** (Early): Through practical drills, the book shows that the first prompt rarely gives the best result. Expect to refine wording, add constraints, and rephrase based on the model’s response—prompting is a dialogue, not a one-shot command. - **Task-specific patterns beat generic advice** (Middle): For summarization, rewriting, grammar correction, and translation, the book provides tailored templates. For example, a good summarization prompt specifies length, focus, and audience, while a translation prompt benefits from stating the target tone and domain. This saves you from reinventing the wheel. - **Context and constraints drive quality** (Middle): The most effective prompts in language tasks include explicit boundaries—what to include, what to ignore, and what format the answer should take. Vague prompts produce vague output; this is the book’s central practical lesson. - **Prompting extends beyond chat** (Late): The same principles apply to data mining and code generation. For programming, the book suggests describing the problem, the expected input/output, and the language or framework—turning a prompt into a mini-specification. This makes the book useful for developers, not just writers. - **AI art needs descriptive prompts** (Late): For image generation, the book emphasizes detailed visual language—subject, style, composition, lighting, and mood—rather than abstract adjectives. This section bridges text-prompting skills to creative visual tasks. - **Platform awareness is practical knowledge** (Ending): The book profiles Baidu Wenxin Yiyan and Alibaba Tongyi, noting their distinct strengths and interface quirks. Knowing which platform handles which task well is part of effective prompting in a Chinese-language context. - **Real-world workflow ties it together** (Ending): The market-research case study demonstrates how to chain multiple prompts—from data gathering to analysis to report drafting—showing that prompt engineering is a process, not a single trick. 【Reading Tips】 - **Skim the taxonomy early, then return to it**: The six prompt types are the backbone of the book. Read that section once, then use it as a reference when you hit later chapters—don’t try to memorize all examples upfront. - **Deep-read the language-task chapters (Middle)**: Summarization, rewriting, grammar, and translation are where the book offers the most reusable templates. Copy these patterns and adapt them to your own work; they are the highest-value pages. - **Treat the drills as exercises, not just reading**: The early practice sections assume you have a model open. If you can, run the examples side by side with the book—the difference between reading and doing is where the learning sticks. - **Skip or skim the platform profiles if you don’t use them**: The Wenxin and Tongyi sections are useful only if you work in a Chinese AI ecosystem. If you use other models, read them for general ideas but don’t linger. - **Use the final case study as a template**: The market-research example is a mini-project. Even if you don’t do research, study how it chains prompts—this is the closest the book comes to showing a full workflow. 【Coverage Limits】 The excerpts cover the book’s table of contents and overall structure but do not include detailed example prompts, specific platform screenshots, or the full text of any chapter. This guide reflects the book’s scope and approach, not its granular content.