提示工程指南
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What is 提示工程指南?
Prompt Engineering Guide is a learning and research resource focused on developing and improving prompts for large language models. It is useful for people who want to understand how model interactions work across question answering, reasoning, writing, coding, and application design, as well as researchers and developers building a more systematic foundation. The site goes beyond a collection of prompt snippets by bringing concepts, methods, applications, model references, and related research into one organized guide.
Readers can begin with prompt components, core concepts, and general design practices before moving into zero-shot and few-shot prompting, chain-of-thought methods, retrieval-augmented generation, and tool use. Its application material covers areas such as data generation, code generation, function calling, classification, information extraction, question answering, summarization, and evaluation, giving users ways to connect techniques with concrete tasks. Dedicated material on risks and misuse, model research, agents, and context engineering also supports readers who want to study broader implementation concerns.
Key features of 提示工程指南
Prompt fundamentals and design
Explains model settings, prompt components, and general design practices so readers can structure inputs and improve interactions with large language models.
Prompting techniques
Covers zero-shot, few-shot, chain-of-thought, self-consistency, and tree-of-thought approaches, helping users compare reasoning and generation strategies for different tasks.
Retrieval and tool workflows
Introduces retrieval-augmented generation, automated reasoning with tools, ReAct, and function calling for studying how models can use external knowledge or tools during a task.
Application examples and exercises
Provides examples for data generation, code generation, classification, information extraction, question answering, summarization, and evaluation, connecting prompting methods with writing, coding, and research work.
Models, research, and risks
Organizes model collections, papers, agent material, retrieval-augmented generation research, trustworthiness topics, and adversarial prompting resources for readers studying model capabilities and limitations.