Prompt Engineering
Core Facts
The core capabilities that people without a technical background should most cultivate when using AI are requirements expression, quality judgment, decomposition thinking, and iteration awareness — not learning to write code
Mainstream Prompt design methods include role setting, task description, output format constraints, few-shot examples, and chain-of-thought.
The foundational section of the course focuses on the underlying principles of Agents, Prompt engineering, and workflow design.
Timeline (last 90 days)
The core skills of the second stage are split into three parts: prompt engineering, RAG (retrieval-augmented generation), and agent architecture design, each paired with a hands-on project, namely RAG document question answering and agent automation workflows.
Prompt engineering is only the entry-level step of Agent development; what really determines whether a project can be delivered is the toolchain and engineering capability.
Prompt engineering in an Agent requires defining roles, available tools, output formats, and stopping conditions, not just designing dialogue templates.
LLM-driven applications typically refer to software products built with large language models (such as GPT, Claude, etc.) as the core reasoning engine, combining external data and tools through techniques such as prompt engineering, RAG, and function calling.
Before modifying the Prompt again, first read several complete records of Agent failures; usually you will find the root cause lies in tool design (misreading tools, being flooded by output, repeatedly retrying on errors it cannot understand), rather than in the Prompt itself.
Reusable visual guidance systems maintain stylistic consistency across different batches of generated results by pre-defining key visual style parameters, and allow for troubleshooting when deviations occur.
The foundational section of the course focuses on the underlying principles of Agents, Prompt engineering, and workflow design.
The core skills of the second phase of the course focus on three areas: Prompt engineering, RAG, and Agent architecture design, accompanied by hands-on projects for RAG document Q&A and Agent automated workflows.
In agent scenarios, minor wording differences in a prompt can lead to completely different behavioral chains, because agents rely on the prompt to decide which tool to call next and whether the task has been completed.
Prompt engineering refers to the technical practice of stably and predictably controlling the quality and format of model outputs by carefully designing the text instructions fed to a large model.
4 more timeline events
All Facts (20)
The core capabilities that people without a technical background should most cultivate when using AI are requirements expression, quality judgment, decomposition thinking, and iteration awareness — not learning to write code
85%VerifiedMainstream Prompt design methods include role setting, task description, output format constraints, few-shot examples, and chain-of-thought.
70%VerifiedThe foundational section of the course focuses on the underlying principles of Agents, Prompt engineering, and workflow design.
65%VerifiedPrompt engineering refers to the technical practice of stably and predictably controlling the quality and format of model outputs by carefully designing the text instructions fed to a large model.
65%VerifiedPrompt engineering is the technique of improving the quality and efficiency of model outputs by optimizing input prompts without changing the model weights.
65%UnverifiedPrompt优化具有零额外基础设施成本、即时部署、无需重新训练、效果可预测且可复现的优势
85%Unverified在大模型时代，Prompt工程、RAG架构设计、Agent编排思维是跨语言通用的核心能力
70%Unverified大模型对指令的敏感度呈非线性分布——主干指令的微小改动可能引发全局行为漂移，而在末尾追加具体的边界条件则相对安全
70%UnverifiedResearch shows that, for the same task, an optimized prompt can improve model output quality by 30%–50% compared to arbitrary input.
60%UnverifiedThe core skills of the second stage are split into three parts: prompt engineering, RAG (retrieval-augmented generation), and agent architecture design, each paired with a hands-on project, namely RAG document question answering and agent automation workflows.
50%UnverifiedPrompt engineering is only the entry-level step of Agent development; what really determines whether a project can be delivered is the toolchain and engineering capability.
50%UnverifiedPrompt engineering in an Agent requires defining roles, available tools, output formats, and stopping conditions, not just designing dialogue templates.
50%UnverifiedLLM-driven applications typically refer to software products built with large language models (such as GPT, Claude, etc.) as the core reasoning engine, combining external data and tools through techniques such as prompt engineering, RAG, and function calling.
50%UnverifiedBefore modifying the Prompt again, first read several complete records of Agent failures; usually you will find the root cause lies in tool design (misreading tools, being flooded by output, repeatedly retrying on errors it cannot understand), rather than in the Prompt itself.
50%UnverifiedReusable visual guidance systems maintain stylistic consistency across different batches of generated results by pre-defining key visual style parameters, and allow for troubleshooting when deviations occur.
50%UnverifiedThe core skills of the second phase of the course focus on three areas: Prompt engineering, RAG, and Agent architecture design, accompanied by hands-on projects for RAG document Q&A and Agent automated workflows.
50%UnverifiedIn agent scenarios, minor wording differences in a prompt can lead to completely different behavioral chains, because agents rely on the prompt to decide which tool to call next and whether the task has been completed.
50%UnverifiedThe design quality of prompts directly determines the performance ceiling of an Agent.
50%UnverifiedProduction-grade prompts need to account for stability, controllability, and edge case handling.
50%UnverifiedPrompt optimization techniques include Chain-of-Thought prompting, self-consistency, the ReAct pattern, and structured output.
50%