Prompt

Agents feed commands and contextual information to large language models as prompts, causing the model to return decomposed steps.

高质量的提示词通常包含四个要素：明确的目标、清晰的约束、具体的上下文和期望的输出格式

Chat Memory simulates memory by injecting historical messages into the Prompt with each request, causing token consumption to grow linearly with the number of conversation turns.

Courses that only teach how to write prompts and have models play roles are more like Prompt courses rather than genuine Agent development courses.

非技术背景的人使用AI最应培养的核心能力是需求表达能力、质量判断能力、拆解思维和迭代意识，而非学习写代码

即使通过Prompt明确要求禁止捏造，模型仍然可能在高置信度场景下输出幻觉内容

Context refers to all the information that the model receives in a single request after the Agent sends it to the large model.

The technical essence of the World Book is a keyword-triggered dynamic knowledge base that automatically injects background knowledge snippets into the Prompt context when specific keywords or semantics appear in the dialogue.

The essence of a prompt is a one-off, stateless session brief, and every time a new session is opened the rules need to be re-argued and re-pasted; this is a structural problem rather than a writing-technique problem.

The natural language description in a prompt differs from the response schema: a schema is an explicit declaration specifying the keys the response object must contain, their types, and whether additional fields are allowed

Prompts should be stored in a version-controlled repository and tagged with semantic version numbers; the major version number corresponds to breaking changes in the output contract, the minor version number to behaviour-enhancing but structurally compatible adjustments, and the patch number to typo and explanatory text fixes.

The prompt should explicitly instruct Claude to treat the HTML as the sole source of truth for visuals, rather than as a replacement for the application's architecture.

The quality of prompts directly affects the output of code generation, and clear requirement descriptions yield better code quality.

Different prompt wordings can significantly affect whether the same model mentions a particular brand.

Understanding how to design prompts and organize a knowledge base often has greater long-term value than familiarity with the interface operations themselves.

A prompt is equivalent to a job description issued to the agent, specifying role positioning, response style, processing logic, and boundary constraints.

Prompts and the knowledge base are the two core elements that determine the output quality of an agent.

The prompt, tool schema, and model version have inconsistent change frequencies and lifecycles: the model version may be upgraded every few months, the tool schema evolves with business iteration, and the prompt may be fine-tuned daily.

When a tool schema changes but the corresponding instruction descriptions in the prompt are not updated in sync, the model may generate malformed tool call requests, leading to runtime exceptions.

Using pure natural-language prompts yields poor results because no technical stack selection or scope definition is performed, which increases token consumption and time usage.