Prompt

大多数 AI 编程智能体无法有效完成复杂任务的主要原因是上下文缺失，而非模型能力不足

Prompts should be written in Markdown format because large models have a more complete understanding of Markdown syntax; their structure can be divided into four modules: role definition, skills, output examples, and constraints.

The core breakthrough of modern AI agents lies in large models endowing agents with the ability to understand natural language instructions and perform reasoning and planning.

Prompt engineering techniques such as chain-of-thought, few-shot prompting, and self-consistency cannot enable a model to access information beyond its training cutoff date, cannot guarantee the precision of floating-point arithmetic, and cannot handle external files the model has never seen.

A Prompt is the instruction text that a user or system inputs to a large model, determining how the model understands the task and the quality of its output.

A prompt refers to any text that a user sends to an AI, including questions or descriptions.

Structured, detail-rich prompts are the decisive factor in getting AI to produce high-quality code, and the precision of the user's description is the true bottleneck of actual output quality.

Pure prompts cannot be engineered; using them yourself is fine, but making them into a product for hundreds or thousands of people to use does not work—there is no version management, no problem tracing, and it cannot withstand large-scale batch tasks.

The hard constraints of formal methods encode safety rules as inviolable logical specifications, perform verification before action execution, operate independently of the model, and cannot be bypassed by attackers through manipulating model inputs.

In the field of AI safety, misalignment refers to a model's actual behavior deviating from the intentions of its designers or users, with specification gaming and reward hacking being related concepts.

When the model itself lacks the capability to understand a specific problem, no amount of prompt engineering can compensate for this deficiency

Prompts have three types of limitations: the capability ceiling cannot be broken through (e.g., training data cutoff, inability to solve complex math problems), output is unstable (changing one word may produce a different result, long prompts cause forgetting), and hallucinations cannot be eradicated.

Prompts and prompt engineering are different: a prompt is a single instruction, while prompt engineering is a methodology that, without modifying model code or parameters, makes the AI stably produce expected results through input design.

The core functions of a prompt consist of four layers: communication bridge, task navigation, style control, and boundary constraints.

A prompt is essentially the neural signal sent to the AI; under the same model, input quality directly determines the output ceiling.

Most current mainstream chatbots implement multi-turn dialogue by "history concatenation": all past messages are stuffed into the prompt, and the model extracts the relevant information itself.

The developer points out that the prompt says "only hand off to a human when the rule clearly holds," which may cause the model to hesitate in borderline cases.

The reason the same set of prompts can be reused across models is that the style description itself carries most of the creative intent, and model differences mainly show up in depth of understanding and generation speed.

The uploader reminds: you need to specify in the prompt whether music and sound effects are needed, otherwise the generated video will have no sound.

Prompts are typically composed of descriptive words for subject, style, image quality, and composition plus weighting syntax; negative prompts are used to exclude unwanted elements.