Prompt
A prompt is a text instruction or question input by a user to an artificial intelligence language model, used to guide the model in generating specific content or performing specific tasks. Its core function is to clearly express requirements, establish context, and set constraints through natural language, thereby influencing the model's output. Prompt design encompasses strategies such as instruction formulation, example provision, and role assignment, and constitutes an important component of human-computer interaction and AI system development.
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
High-quality prompts typically contain four elements: a clear objective, explicit constraints, specific context, and the desired output format.
A Skill is the concept of packaging multi-step interaction logic, context management, and output constraints into a callable unit, distinct from a single-interaction prompt template.
Timeline (last 90 days)
The technology selection judgment process is: first prepare test data to verify feasibility; when basic capabilities are insufficient, prioritize RAG; when external systems need to be connected, use Function Calling; and only consider fine-tuning in scenarios such as improving stability, reducing inference costs, and private deployment.
An Eval system requires a test set with standard answers or scoring criteria, scoring via rule matching, similarity computation, or model-as-judge, and running it after every prompt change, model swap, or retrieval strategy adjustment to use data to judge whether the change is an improvement or a regression.
Managing prompt templates, tool schemas and test cases as the same 'Agent contract' (versioned, in the same repository, with linked review on changes) can make the scope of impact of a model switch visible.
Storing the full prompt and completion text for every production request causes costs to rise sharply; a high-throughput service processing tens of millions of tokens per day generates several GB of unstructured strings.
Prompt-driven tasks usually can only run all the way to the end, and any step failure may cause the entire process to restart, and there is no persistent state, no way to control context growth, and no deterministic validation of tool results.
When output deviates from requirements, check the Prompt layer; when a task breaks off, check the Context layer; when data transmission goes wrong or the process cannot be recovered, check the Harness layer; when multiple Agents wait for each other, check the collaboration scheduling layer.
Modifying the behavior logic part of a prompt (tool descriptions, output format, guardrails, stop instructions) will not immediately throw an error, but it will cause the Agent's behavior to drift silently.
The PM's prompt modifications must first pass on the evaluation set before going live, including several complete Agent runs to detect tool selection drift.
The team plans to split the prompt into templates: product-related paragraphs become variables owned by the PM, and the rest stay in the code review process.
A prompt can be split into two categories: pure product content (tone, wording, few-shot examples, fallback scripts, pricing explanations) and behavior logic that is more like code (tool descriptions, output format, guardrail instructions, stop instructions).
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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%VerifiedHigh-quality prompts typically contain four elements: a clear objective, explicit constraints, specific context, and the desired output format.
80%VerifiedA Skill is the concept of packaging multi-step interaction logic, context management, and output constraints into a callable unit, distinct from a single-interaction prompt template.
80%VerifiedLarge language models can assist in generating initial script drafts, refining expression, and generating course outlines.
75%VerifiedAgents feed commands and contextual information to large language models as prompts, causing the model to return decomposed steps.
65%VerifiedChat 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.
65%VerifiedIn multi-turn conversations, the System message sets the behavioral tone and constraints, the user message represents user input, and the assistant message is the model's historical replies.
65%Unverified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
90%UnverifiedContext refers to all the information that the model receives in a single request after the Agent sends it to the large model.
60%UnverifiedThe 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.
60%UnverifiedThe technology selection judgment process is: first prepare test data to verify feasibility; when basic capabilities are insufficient, prioritize RAG; when external systems need to be connected, use Function Calling; and only consider fine-tuning in scenarios such as improving stability, reducing inference costs, and private deployment.
50%UnverifiedAn Eval system requires a test set with standard answers or scoring criteria, scoring via rule matching, similarity computation, or model-as-judge, and running it after every prompt change, model swap, or retrieval strategy adjustment to use data to judge whether the change is an improvement or a regression.
50%UnverifiedManaging prompt templates, tool schemas and test cases as the same 'Agent contract' (versioned, in the same repository, with linked review on changes) can make the scope of impact of a model switch visible.
50%UnverifiedStoring the full prompt and completion text for every production request causes costs to rise sharply; a high-throughput service processing tens of millions of tokens per day generates several GB of unstructured strings.
50%UnverifiedWhen output deviates from requirements, check the Prompt layer; when a task breaks off, check the Context layer; when data transmission goes wrong or the process cannot be recovered, check the Harness layer; when multiple Agents wait for each other, check the collaboration scheduling layer.
50%UnverifiedPrompt-driven tasks usually can only run all the way to the end, and any step failure may cause the entire process to restart, and there is no persistent state, no way to control context growth, and no deterministic validation of tool results.
50%UnverifiedA prompt can be split into two categories: pure product content (tone, wording, few-shot examples, fallback scripts, pricing explanations) and behavior logic that is more like code (tool descriptions, output format, guardrail instructions, stop instructions).
50%UnverifiedThe PM's prompt modifications must first pass on the evaluation set before going live, including several complete Agent runs to detect tool selection drift.
50%UnverifiedModifying the behavior logic part of a prompt (tool descriptions, output format, guardrails, stop instructions) will not immediately throw an error, but it will cause the Agent's behavior to drift silently.
50%UnverifiedThe essential difference between workflow orchestration and mere Prompt writing is: Prompt focuses on the input and output of a single interaction, while workflow focuses on data flow and control logic among multiple nodes (execution order, conditional branches, retry and rollback, parallelism, etc.).
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