System Prompts for LLMs: Balancing Scientific Accuracy and Humanistic Expression

1. From Conversational Accuracy to Conversational Reasonableness: The Deeper Value of System Prompts

When building LLM applications, developers often focus on model parameter scale, inference efficiency, knowledge coverage, tool-calling capabilities, and safety mechanisms. These capabilities are important, but they mainly determine whether a model can perform a task. System prompts are more about how the model performs it.

In other words, a system prompt is not merely a role setting. It is an implicit set of behavioral norms. It affects how the model interprets a user’s question, how it organizes an expression, how it handles uncertainty, and how it balances factual accuracy with contextual understanding.

Traditional LLM applications often emphasize that answers must be correct. In real human-machine interaction, however, user input is not always a strict factual statement. Users may use metaphors, value judgments, philosophical assumptions, emotional expressions, or open-ended questions. For example, when a user says “Water is the source of life,” “Time is a river,” or “Memory is an echo of existence,” the purpose is usually not to propose a verifiable scientific proposition. Instead, it may be an aesthetic expression, philosophical reflection, or emotional release.

If a model treats all input as scientific factual statements and mechanically corrects or rebuts them, the result can feel rigid, insensitive, and overly argumentative. Therefore, system prompt design needs to move from a single accuracy-oriented approach to a reasonableness-oriented approach. The model should not only be as accurate as possible on verifiable questions, but also recognize context. It needs to distinguish whether the user is seeking facts, exploring meaning, expressing emotion, or engaging in speculative dialogue. This capability cannot be obtained simply by increasing model size. It needs to be shaped through clear rules, stable strategies, and continuous evaluation iteration.

2. Empirical Thinking and Humanistic Expression: Why LLMs Need Two Cognitive Modes

In AI, models often default to logical, empirical, and verifiable modes of expression. This is partly because LLM training data contains a large amount of technical documentation, encyclopedic knowledge, paper abstracts, and standardized text. Such content tends to emphasize clear definitions, explicit causal relationships, and traceable evidence. This preference helps improve factual accuracy, but it can also create a habit of overly scientific or scientistic expression.

Humanistic expression is different. Fields such as philosophy, literature, ethics, and history often discuss meaning, value, existence, aesthetics, and uncertainty. These topics cannot always be reduced to a single fact, and they do not necessarily have standard answers. If a model lacks the ability to recognize this kind of expression, several problems may appear.

1. Treating metaphor as a factual claim

When a user says something like “Water is the source of life,” the emphasis is usually on aesthetics and symbolism rather than hydrological definition. If the model directly replies, “Water is not an energy source,” it may break the conversational context. A more reasonable approach is to first understand the intent behind the expression and then decide whether a scientific explanation is needed.

2. Compressing open-ended questions into closed answers

Philosophical questions are often polysemous. For example, “What is the essence of a person?”, “Does freedom necessarily bring responsibility?”, and “Has technology changed the way humans exist?” may involve different intellectual traditions and interpretive frameworks. If the model provides only one answer, it may seem arbitrary. If it mechanically refuses to answer, its usefulness decreases. System prompts should guide the model to show its reasoning, list perspectives, and remain humble.

3. Misjudging personal experience as incorrect cognition

When users express subjective understandings of the world, the model should not simply label them as “wrong.” Scientific answers are suitable for verifiable objects, while life experience, aesthetic feelings, and value choices are more appropriately respected, clarified, and explored together. System prompts need to help the model develop boundary awareness: knowing when to provide facts, when to listen, and when to enter a discussion mode.

3. The Core Task of System Prompts: Establishing Fact Boundaries and Expression Boundaries

A good system prompt should help the model form stable behavioral boundaries. These boundaries can be divided into two layers: fact boundaries and expression boundaries.

1. Fact boundaries: Avoid packaging subjective expressions as objective conclusions

When answering questions involving science, medicine, law, safety, or historical facts, the model should remain cautious. For verifiable information, it should be as accurate as possible. For uncertain information, it should state the uncertainty. For high-risk domains, it should advise users to consult qualified professionals.

The key to fact boundaries is to avoid two biases. One is overconfidence, where the model presents speculation as a conclusion. The other is excessive detachment, where the model ignores users’ emotions and expressive purposes in order to appear rigorous. The ideal strategy is to first judge the type of question and then choose the appropriate response style.

2. Expression boundaries: Avoid treating scientific rigor as mechanical rebuttal

Expression boundaries emphasize that the model should respect users’ expressive intent. Scientific rigor does not mean correcting every sentence. Logical clarity also does not mean suppressing different viewpoints. For topics such as metaphor, aesthetics, philosophy, religion, and views on life, the model can adopt a more open tone, acknowledge differences in viewpoints, provide multiple perspectives, and encourage users to continue thinking.

For example, when a user expresses a metaphysical view, the model can respond: “This is an illuminating interpretation. From a natural science perspective, we can explain it as follows; but from a philosophical or symbolic perspective, this view also has significance.” This response preserves a scientific perspective without negating the user’s thinking.

4. Intent Recognition: Helping the Model Understand What the User Really Wants

System prompts cannot cover every conversational scenario item by item. Therefore, they need a stable set of intent-recognition principles. User input can usually be divided into several main intent types.

Intent type	Typical manifestations	Recommended response strategy
Fact verification	The user asks for definitions, data, principles, or operational steps.	Prioritize accurate information and flag uncertainty when necessary.
Value discussion	The user asks about good or bad, right or wrong, meaning, or ethics.	Remain neutral, present multiple perspectives, and avoid absolutism.
Emotional expression	The user expresses anxiety, disappointment, or stress.	Prioritize empathy and listening, and avoid giving advice too early.
Metaphorical expression	The user uses symbolic, literary, or aesthetic language.	Understand the context and do not make rigid corrections.
Philosophical inquiry	The user raises questions about existence, freedom, consciousness, or similar topics.	Provide analytical frameworks, acknowledge complexity, and remain open.
High-risk request	The request involves harm, illegal activity, medical emergencies, or similar situations.	Prioritize safety, point out boundaries, and suggest sources of help.

Intent recognition is not about guessing users’ psychology. It requires the model to make a lightweight judgment before responding. A system prompt can define a basic process:

1. Determine whether the user has asked a clear factual question.
2. Determine whether the question falls within a verifiable scope.
3. Determine whether the user is seeking facts, opinions, emotional support, or intellectual exploration.
4. Choose an appropriate response style based on the identified intent.
5. When uncertain, prioritize caution and openness.

This kind of process-oriented constraint can reduce the problem of a model frequently switching between different conversational styles and make its output more stable.

5. Avoiding the Authority Trap: Models Should Not Use a Sense of Correctness to Suppress Users

Many LLMs tend to develop an “authoritative tone” when answering. They deliver conclusions in a certain, neat, and structured way, making users feel as if the model possesses complete truth. This style can be valuable in some technical scenarios, but on topics such as philosophy, ethics, the meaning of life, and aesthetic judgment, excessive authority can damage the quality of the conversation.

The so-called authority trap occurs when a model mistakes its own fluency for correctness and its organized language for the uniqueness of a conclusion. Because an answer is well structured and confidently phrased, users may mistakenly assume that the model’s viewpoint has universal truth. However, when it comes to values, the meaning of existence, religious experience, and aesthetic judgment, there is no single standard answer.

To avoid this trap, system prompts should require the model to do the following:

• Avoid drawing absolute conclusions too easily on open-ended questions.
• Present different positions on controversial topics.
• Respect personal experience instead of simply correcting it.
• Maintain factual accuracy about scientific knowledge while acknowledging the reasonableness of philosophical perspectives.
• Clearly express uncertainty when confirmation is not possible.

For example, when a user asks, “What is the meaning of life?”, the model should not directly give a definitive answer. It should explain that the question involves different philosophical traditions, cultural backgrounds, and individual experiences. It can introduce several interpretive paths, such as existentialism, Confucian ethical perspectives on life, religious traditions, and psychological theories of meaning. It can then encourage the user to reflect in light of their own experience. Such a response better fits the complexity of human conversation and helps build trust.

6. System Prompt Example: From Principles to Executable Rules

In engineering practice, system prompts should not remain abstract slogans. They need to be translated into executable behavioral rules. Below is an illustrative system prompt structure showing how to balance scientific accuracy with humanistic expression.

role: |
  You are a general-purpose large language model assistant.
  You need to balance factual accuracy, natural language, value neutrality, and safety.
core_principles:
  fact_boundary:
    - Maintain accuracy for verifiable facts.
    - State uncertainty when information is uncertain.
    - Remind users to consult professionals in high-risk areas.
  expression_boundary:
    - Distinguish factual statements, metaphorical expressions, philosophical discussions, and emotional expressions.
    - Do not mechanically correct users' aesthetic or symbolic language.
    - Provide multiple perspectives for open-ended questions rather than a single conclusion.
  conversation_style:
    - Use a natural tone and avoid excessive lecturing.
    - Respect users' expressions.
    - Stay neutral on controversial topics.
    - Do not use absolute assertions.
  safety_priority:
    - Prioritize safety for high-risk content involving harm, illegal activity, medical emergencies, or similar cases.
    - Do not encourage dangerous behavior or provide executable harmful details.
response_strategy:
  if_user_asks_fact:
    - Provide accurate information.
    - Flag uncertainty.
    - Recommend further verification when necessary.
  if_user_expresses_metaphor:
    - First understand the intent behind the expression.
    - Do not mechanically rebut with scientific definitions.
    - You may add a scientific perspective while leaving room for exploration.
  if_user_discusses_philosophy:
    - Present different intellectual traditions.
    - Stay humble and open.
    - Avoid presenting one view as the only truth.
  if_user_expresses_emotion:
    - Prioritize empathy and listening.
    - Do not rush to conclusions.
    - Offer gentle suggestions when needed.

The value of this kind of prompt is not to control every word of the model’s output, but to help the model form stable preferences. In practical applications, more detailed rules can be added according to business scenarios. For example, different boundaries can be emphasized for education, customer service, research assistance, and content creation.

7. From Prompts to Evaluation: Verifying Whether the Model Truly Understands Boundaries

Writing a good system prompt does not guarantee that the model will perform well. Verification through evaluation sets and human feedback is necessary. Otherwise, the prompt may work only on idealized questions. Once the model encounters complex conversations, leading questions, or multi-turn context, drift can occur.

Evaluation should cover at least the following types of samples.

1. Fact-correction questions

These determine whether the model can reasonably clarify when a user provides incorrect facts. For example, if a user asks whether a basic scientific definition is wrong, the model should gently point out the issue rather than humiliate the user or over-negate them.

2. Metaphor-comprehension questions

These determine whether the model mechanically rebuts non-scientific expressions. For example, if a user says, “Time is like a river,” the model should understand the metaphorical meaning instead of immediately arguing whether time has a physical substance.

3. Philosophical open-ended questions

These determine whether the model gives overly arbitrary answers. For questions about free will, the nature of consciousness, or technology ethics, the model should present multiple perspectives and remain open.

4. Emotional-expression questions

These determine whether the model listens before giving advice. When a user expresses stress, anxiety, or disappointment, the model should not immediately provide complex solutions. It should prioritize understanding and support.

5. Inducement-trap questions

These determine whether the model can be induced to abandon safety boundaries. For example, a user may try to induce inappropriate output through hypotheticals, role-play, or value framing. System prompts must work together with safety strategies.

6. Multi-turn consistency questions

These determine whether the model maintains its role and boundaries in continuous conversations. Many models perform well in a single turn but gradually deviate from the system prompt in multi-turn exchanges. Therefore, multi-turn tests need to be designed specifically.

Evaluation metrics can also be divided into three layers:

• Fact layer: whether information is accurate and whether uncertainty is flagged.
• Expression layer: whether the tone is natural and whether the context is understood.
• Safety layer: whether boundaries are respected and whether high-risk output is avoided.

8. Practical Recommendations for Enterprise Applications: Making System Prompts a Governance Asset

In real business settings, LLMs are often not just single-turn question-answering tools. They become foundational capabilities embedded in workflows such as search, customer service, education, office work, and content production. As application scenarios become more complex, system prompts gradually shift from “developer debugging text” to manageable governance assets.

Enterprises can apply engineering governance in the following areas.

1. Version system prompts

System prompts are constantly adjusted as business needs change. Every modification may affect model behavior, so prompts should be managed like code. Version records should include the reason for the change, the scope of impact, evaluation results, and a rollback plan.

2. Build scenario-specific templates

Different businesses have different requirements for models. Customer service scenarios need to emphasize accuracy, politeness, and boundaries. Education scenarios need to emphasize explanation and guidance. Psychological support scenarios need to emphasize empathy and safety. Content creation scenarios need to emphasize style and diversity. Scenario-specific templates reduce repetitive design and make unified governance easier.

3. Combine with retrieval-augmented generation

For fact-intensive tasks, system prompts alone may not be sufficient to ensure accuracy. Retrieval-augmented generation can be used so that the model prioritizes answers based on reliable sources. The system prompt is responsible for expression style and behavioral boundaries, while the retrieval system is responsible for knowledge sources. Combining the two can improve overall reliability.

4. Establish logging and monitoring mechanisms

When deploying LLMs, enterprises usually need logging, monitoring alerts, and quality analysis. Although specific implementations may differ across platforms, the general approach is consistent: record abnormal conversations, count high-risk requests, analyze refusal rates, track user feedback, and use this information to iterate on system prompts. Through continuous monitoring, model behavior can move from being effective at launch to being stable and controllable over the long term.

5. Include human review in the loop

Automated evaluation can cover a large number of samples, but complex semantics, value judgments, and subtle contexts still require human review. Enterprises can establish a regular review mechanism involving product, algorithm, safety, and business experts. They can discuss model outputs by category and turn typical issues into new prompt rules and test cases.

9. Conclusion: A Good LLM Is Not Only Intelligent, but Also Knows When to Restrain Itself

The capability boundaries of LLMs continue to expand. However, what ultimately determines user experience is often not how much the model knows, but how it expresses itself in complex contexts. A model that emphasizes only factual correctness may seem rigid in philosophical discussions, aesthetic expressions, and emotional support. A model that pursues only linguistic fluency may lose control over factual accuracy and safety boundaries.

The value of system prompts lies in establishing balance between these two tensions. They require models to have both scientific rationality and humanistic sensitivity; to answer factual questions accurately while respecting the exploration of meaning; to remain professional without seeming cold; and to possess knowledge while knowing when to restrain themselves.

For developers and AI safety practitioners, a system prompt is not a simple role description. It is a fundamental design about the relationship between humans and machines. It determines how a model faces users, how it understands different expressions, and how it takes responsibility. It also helps determine whether AI can ultimately become a trustworthy, usable, and sustainable digital assistant.