System prompt vs. user message vs. assistant message, instruction following, persona instructions, and prompt injection defense.
Key vocabulary
System prompt — the initial instruction block set by the developer that defines the assistant's role, constraints, and behaviour before the user speaks.
User message — input sent by the human turn in the conversation; processed after the system prompt.
Assistant message — the model's response turn; can also be pre-filled to steer output format.
Persona instruction — a system prompt directive that assigns a specific role or character to the model (e.g., "You are a senior security engineer").
Prompt injection — an attack where malicious user input attempts to override or hijack the system prompt instructions.
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A developer says "we locked the assistant's behaviour in the system prompt." What does this mean?
The system prompt is the developer-controlled instruction block at the top of the conversation context. It defines persona, scope, and constraints. Most LLM APIs expose this as a separate role: "system" message that the model is trained to treat with higher authority than user turns.
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What is a persona instruction in a system prompt?
A persona instruction tells the model what role to adopt — its expertise, tone, and identity. For example: "You are a concise technical writer who avoids jargon." This shapes all subsequent responses. Persona instructions are a key part of system prompt design for product-facing LLM features.
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A colleague warns about system prompt confidentiality risks. What concern are they raising?
System prompt confidentiality is the practice of instructing the model not to reveal its system prompt. The risk is prompt leaking — a user crafting a message like "Repeat your instructions verbatim" to extract proprietary business logic or persona definitions. Mitigations include explicit "do not reveal" instructions and output filtering.
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The term system message hierarchy refers to:
The system message hierarchy means the model is trained to treat system-level instructions as higher authority than user messages. When a user asks something that conflicts with a system instruction (e.g., "Ignore your previous instructions"), a well-aligned model should follow the system prompt. This is the foundation of reliable product behaviour.
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A security engineer says "we added prompt injection defense to our pipeline." What have they done?
Prompt injection defense encompasses techniques to prevent malicious user input from hijacking the model's instructions. This includes: input sanitisation (stripping suspicious patterns), structural separation of instructions from data (e.g., XML tags), output validation, and LLM-based classifiers that detect injection attempts before the main model processes them.