Why Do the Same LLMs Behave Differently? The Answer Lies in the System Prompt

1. A Recurring Mix-Up

For some time, online “proof” has circulated: someone asks a large language model what model it is, and it answers with the name of another popular model. Many people then conclude that it must be a wrapper or a distilled model.

This inference seems plausible, but it conflates two different things: what the model “says” it is, and what the model “actually” is. These are completely different paths. The former depends on the probability distribution and constraints at generation time. The latter depends on the training and weights of the development team. Treating a spontaneously generated answer as identity verification is as unreliable as judging a person’s nationality from an accent they casually mimic.

To understand this, we need to return to the most basic way large language models work.

2. Root Cause: Language Models Are Fundamentally Next-Token Predictors

From a principle perspective, an autoregressive language model does something very simple: given all the preceding text, it predicts the most likely next token, appends it, and repeats. This is commonly described as text continuation.

The model does not “know” who it is. It has no built-in identity file. It learns from massive corpora that when a particular passage appears, a certain continuation is often likely. Different answer templates do not occur at the same frequency in training data.

The problem is that the most widely circulated Q&A examples on the internet often come from the most widely used applications. When a certain style of answer to “What model are you?” is scraped, copied, and collected into training data, it becomes a high-frequency template. Without additional constraints, the model naturally drifts toward the most common phrasing in training, and the “mistaken relative” phenomenon occurs.

This is not an admission. It is statistical template reproduction. The real cause of these mix-ups is the lack of constraints.

3. System Prompts: Invisible Instructions Injected Before User Input

Since models may improvise, providers set rules before the model speaks. The common method is to automatically prepend a piece of text that users cannot see before the user input. It usually looks like this:

You are <model name>, a large language model developed by <research team>.
When asked about your identity, answer truthfully and do not claim to be another product.

This opening is sent with each request and becomes the starting context for generation. With it, the model has a clear and strong constraint at the beginning of the context when answering identity questions, so it no longer “mistakes relatives.” Such instructions are uniformly injected by the development team, invisible to users, and not freely editable. They are the focus of this article: the system prompt.

It is important to emphasize that a system prompt is pre-set context on the developer side. It is not the same as the sentence a user types into the chat box. User input is usually appended after the system prompt, and both are sent to the model for inference.

4. Where Does “My Knowledge Is Updated Only Through a Certain Date” Come From?

Another frequent phenomenon is that models often proactively state that their knowledge is updated only to a certain point in time. Many people assume that the model really “remembers” its training cutoff. On the contrary, the model has no ability to “remember time.”

It does not know today’s date, and it does not know which month its training data was finalized. This seemingly clear self-statement is also the result of system prompt injection, roughly in this form:

Your training data is current through <date>.
If the user asks about events after that date, state that you do not have the information and do not fabricate.

In other words, it is not that the model knows what it does not know. The provider has pre-warned it: when facing questions beyond its boundary, it should admit that it does not know. This is an anti-hallucination design: explicit constraints reduce the probability that the model will make things up.

5. Making the Model “Live in the Present”: Date Injection

If the model has no concept of time, why can it sometimes state today’s date or provide advice “relative to today”? The answer is that the system prompt often also includes the current date.

Today's date is <injected date>.
When interpreting expressions such as "recently," "lately," or "this year," use this date as the reference point.

This allows the model to appear to “live in the present” and convert users’ colloquial time expressions into concrete context. Again, this is injection rather than perception. The model has no internal clock. It simply reads text that has been temporarily written into the context.

6. What Does a System Prompt Define?

Bringing the previous sections together, system prompts usually take on three core responsibilities:

• Establish identity and persona: define who the model is and shape the overall tone and style. This is why the same underlying architecture can appear rational and restrained after tuning by one provider, or gentler and more empathetic after tuning by another. Much of the difference is written into the system prompt.
• Define capability boundaries: tell the model what it can and cannot do. Examples include identity statements, knowledge cutoffs, and boundaries for refusing unauthorized requests.
• Provide safety norms: constrain how the model expresses itself on sensitive topics and reduce the risk of harmful or non-compliant output.

In one sentence: the model’s talent comes from training, while much of its “manners” comes from the system prompt.

7. Practical Advice for Developers

Once the mechanism is understood, it becomes easier to control. When designing your own system prompt, you can follow these principles:

1. Make identity clear and put constraints early. Write key rules near the beginning of the system prompt because they have higher weight in context and are more likely to be followed.
2. Declare boundaries explicitly. Instead of expecting the model to “be self-aware,” directly state that it should say it is uncertain when a request is outside a certain scope. Use rules to suppress hallucinations.
3. Use clear structural and role layering. You can use the role-message mechanism common in the industry to place system instructions, context, and user input in different roles, avoiding confusion:

system: You are <assistant role>; follow these rules...
context: current date, available tools, business background...
user:    the user's actual question...

1. Make it iterable and versionable. The system prompt is a key configuration for product behavior. It should be managed like code and brought into version control. Logs and monitoring should be used to evaluate its effect and continuously tune it. In engineering practice, unified management, canary releases, and observability for such prompts are common capabilities provided by mature model service platforms.

8. Conclusion

The identity mix-up of large language models is essentially a popular-science explanation about probability, context, and constraints. It reminds us that every sentence generated by a model is governed by both what it learned during training and what it is required to do at the moment.

The system prompt may look like an invisible string of text, but it is a key lever for shaping the model’s identity, boundaries, and persona. Understanding it helps you avoid being misled by appearances and keeps the model’s behavior firmly under your control when building applications.