System Prompt

System prompts are a layer of instructions preset by vendors and invisible to users, injected into the model context before the user's conversation begins, used to shape the model's behavioral boundaries, response style, and safety policies.

An AI tool reading a context configuration file at startup is equivalent to injecting a project-level system prompt into the model before each conversation, thereby reducing low-quality modifications caused by a lack of background knowledge.

For conversational AI services, the model must read plaintext input to generate a response, making it fundamentally contradictory and impossible for both conditions to hold simultaneously: that the service provider cannot read user input and that the AI can understand the response content.

The constraints of system prompts and safety alignment are probabilistic and cannot guarantee that the model triggers a seek-medical-care reminder under every urgent phrasing.

Appending rules to the system prompt when the model makes a mistake makes the Agent perform worse, because the system prompt is always resident, and every added character is fed into the model again at every reasoning step, continuously consuming attention and token budget.

In terms of concrete implementation, the system prompt needs to include risk-identification logic for specific scenarios as well as structured refusal language: first state the identified problem, then explain the potential consequences, and finally make clear that the user can still decide for themselves.

System prompts differ from capabilities baked into model weights during training: they are a kind of "runtime rule" that can be flexibly adjusted at the deployment level.

The system prompt is a high-priority instruction injected by the application developer, but the model's compliance with the system prompt is only a statistical tendency rather than a mandatory constraint, so the system prompt cannot serve as a reliable trust boundary.

Converted using the cost weights set by the author, the fixed context made up of the system prompt plus the tool list accounts for 43.3% of Claude Code's total cost.

The system prompt contains the agent's behavioral guidelines, safety constraints, and output format requirements; the tool list declares the names, descriptions, and parameter definitions of all callable tools in the form of JSON Schema.

Prompt injection attacks exploit large language models' tendency to follow instructions, inducing them to ignore the original system instructions, so security mechanisms that rely on prompts carry the risk of being bypassed.

In the context of custom agents, Instructions refer to system-level prompts written in natural language that define the agent's role, goals, tone, available tools, information priorities, and boundary constraints.

The usual ways to compensate for an Agent's lack of risk perception are to explicitly mark danger levels in the system prompt, write constraints into tool descriptions, or construct in-context experiential memory by retrieving historical incident cases.

Production-level system prompts should be treated as versioned specifications, with every change reviewed as rigorously as a code review.

The system prompts of the vast majority of mainstream assistants emphasize safety first, including refusing harmful requests, not providing guidance on dangerous actions, and acting conservatively under uncertainty.

A system prompt is a default behavioral script written by the vendor for the model, determining how the AI behaves by default when faced with various requests.

Generative recommendation systems draw on the modeling approach of large language models, treating user behavior sequences as a generation task and predicting the next item of potential interest in an autoregressive manner.

Periodically re-injecting core policies into the context during long conversations can prevent them from being diluted by a large volume of subsequent content.

The test team's core conclusion is that blacklists and system prompts are useless against transactional side effects.

Preferences, background, and commonly used formats can be codified through system prompts or custom instructions, avoiding the need to repeat them every time.