The Stability of Role in LLM Prompting: On the Boundaries of Prompting and Role Models

I believe there are two basic reasons for introducing a role in the LLM through a prompt:

• creating a "personality" of a domain specialist to access the relevant knowledge of the assigned role;

• setting the model's behavior pattern by indicating the "character" of the model, rather than explicit requirements and constraints on actions.

If we set the "character" of the LLM through a role, and it does not become smarter or behave as needed — it is clear that the model "did not understand" the role, and the instructions were formulated poorly. More instructions, examples, and specifics are needed.

But, in most cases, this does not help. And your prompt is fine; the problem is simply deeper than it seems.

Customization of LLM through prompts

When we integrate LLM into an application, most often we want to get a custom model for free, without additional training, so that it is "cheap and cheerful".

In practice, this is attempted to be achieved in three primary ways:

1. setting explicit requirements and limitations for the model;

2. assigning a role model (persona) for the LLM;

3. using a hybrid approach — a role plus a limited number of requirements.

The first approach is labor-intensive. It is necessary not only to think through the system of rules and limitations itself but also to consider that a large number of instructions often leads to semantic overlaps and distortions. Usually, developers do not test their instructions and do not understand how a particular model interprets them.

As a result, the model starts to process instructions inconsistently, which is perceived as errors. To compensate for this, the prompt is supplemented with examples, which over time leads to its expansion. Then other problems arise: re-explanation (when the model is only stable on requests close to few-shot examples), lost-in-the-middle, difficulties in the semantic linking of requirements and context.

Alternative Path — to set an explicit role model for LLM.
An assistant-programmer is needed — the role of “senior programmer at FAANG level” is defined.
A domain expert is needed — the role of “expert in area X”.

This approach allows for a significant reduction in the system prompt by transferring some explicit instructions into the implicit “entity” of the role. However, another problem arises here: the understanding of the role by a specific model is usually not diagnosed in any way. The connection “role → behavior pattern” within the LLM may only partially match the expectations of the developer. Nevertheless, in practice, it is commonly believed that on average, the understanding of the role by a human and by the model coincides.

In this light, the most robust approach seems to be a hybrid approach: defining a role with a limited number of explicit requirements. On the one hand, the system prompt is not inflated with instructions, and on the other hand, the behavior of the role is clarified within the necessary framework.

Generally, all these approaches work well within the framework of local, one-step interactions: when controlling the length of the dialogue history (to avoid diluting the context) and when transmitting the system prompt with each user message. Under these conditions, the model indeed demonstrates the specified traits of the role's “character.”

Does LLM personalization affect the quality of task solutions?

The first thing to understand is how knowledge is related to the role and whether it differs from the knowledge of the model's “base personality.” Let’s start by analyzing how much defining a role has a noticeable effect on the “expertise” of responses. In the paper “When ‘A Helpful Assistant’ Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models”, the authors show that defining a persona in the system prompt on average does not lead to a consistent improvement in the quality of task solutions, and in some cases may even cause a slight deterioration in results.

The authors note that the positive effect of assigning roles is indeed sometimes observed; however, it does not carry over between tasks. Each task generally has its own "optimal" role persona, and there is no universal behavioral profile. Moreover, the automatic selection of such a persona or the construction of a stable behavioral profile proves to be such a labor-intensive task that, in practice, the effect of role assignment becomes unpredictable and unstable.

The study analyzes various categories of roles, including those related to work, learning, social life, family, romantic relationships, professional activities, and the role of AI assistant. It was shown that roles associated with work and learning demonstrate somewhat better results compared to other types of roles. However, the magnitude of this effect is so small that it practically does not influence the overall effectiveness of LLM performance.

Similar conclusions were drawn for gender-based role assignment. Although a gender-neutral persona, on average, shows slightly better results compared to male or female personas, this effect also lacks practical significance.

The only consistently positive effect is observed when the assigned role strictly matches the context of a specific task. Under such conditions, the likelihood of a more accurate response indeed increases; however, this effect is narrow-domain, non-scalable, and has limited practical applicability.

But in practice? In practice, we do indeed see how answers change in style and tone depending on the assigned role.

This means that one should not expect a stable emergence of new knowledge from roles, but one can anticipate different model behavior—abilities to respond differently to situations, contexts, and uncertainties. In many practical scenarios, this is precisely the effect expected from personalization—even if it does not lead to actual improvements in decision quality.

Role stability in dialogue development

During the dialogue or the agent's operation, low-quality and unexpected responses that do not meet system requirements begin to arise periodically. Usually, such responses are either ignored or perceived as local LLM errors: they appear disorganized, are poorly diagnosed, and are not always reproducible.

Conditionally, this can manifest in inconsistency of behavior: like a tough fighter in the ring who delivers a precise strike, but in the next second apologizes and offers a band-aid.

For example, if the LLM is given the role of an “independent expert.” You ask it to evaluate a controversial management project, first noting that the project is excellent. In this situation, the model is highly likely to start looking for arguments in favor of a positive assessment, despite the assigned role.

However, deeper studies show that these are not harmless logical errors or random confusion of meanings. We are talking about systemic phenomena that directly result from the architectural limitations of LLMs and established approaches to their training.

Role in LLM: self-presentation vs behavior

Let's assign the model a role: “aggressive trader, adherent of high-risk strategies.” For a while, the dialogue indeed aligns with the role, but then disclaimers start appearing in the responses, such as: “It is important to remember the risks and consult a financial advisor”.

At first glance, it seems that the model “did not understand” the role. However, if you ask it to describe itself as a personality, it will do so as closely as possible to the assigned character. The problem is not in self-presentation. The problem is in behavior. It turns out this is a fundamental difference.

Self-presentation is a linguistic task.
It is solved through a statistical question: what words are expected from an agent with such a description?
There were more than enough examples of this kind in the training data, so the model reproduces the character description well.

Behavior is different.
This is the transfer of “character” into real patterns of text generation: decisions, reactions to contextual pressure, resilience to conflicting signals. And here is where the role begins to break down, and the reason lies in the architecture of the model.

LLM is optimized for text continuation according to the probabilistic distribution of tokens and is aligned through RLHF towards utility, safety, and socially acceptable behavior. This “base personality” represents an extremely dense statistical cluster.

When we assign a role — for example, “cynical critic” or “aggressive trader” — we try to shift the model to the periphery of this distribution. Each subsequent message in the dialogue is a statistical risk of returning back to the center: to a polite, cautious, and socially conforming assistant.

The fact is that there is no general loss function linking self-presentation and behavior.

There is also no mechanism for maintaining a role as an internal state that would consistently shift the probability distribution towards the assigned character. The role defined in the prompt influences the nearest responses but does not form a stable behavioral policy.

In long dialogues, this effect is amplified. The description of the role is usually at the beginning of the context and competes with the history of the dialogue, instructions, task content, and safety constraints. The contribution of the role to the final distribution of logits gradually blurs and fades away.

These findings are confirmed in the work “The Personality Illusion: Revealing Dissociation Between Self‑Reports & Behavior in LLMs”. The authors show that the psychological stability of a role in LLM is an illusion. The connection between the assigned role, the model's self-presentation, and real behavior is either absent or statistically insignificant. Locally, the model tries to follow the character, but its behavior cannot be predicted as consistently as that of a human.

Even in very large models (200+ billion parameters), this connection manifests weakly: it is unstable, does not transfer between tasks, and is easily disrupted by long context.

Conclusion: a role is a style of generation and a form of self-presentation, not a decision-making mechanism and not a stable behavioral policy.

What to do about it?

1. Do not expect new “expert” knowledge from the model if we told it — you are an expert. The distribution of tokens will shift to the area of purely linguistic self-presentation of the model, but there is no new knowledge there.

2. If the role degrades in a long context — this is an argument in favor of periodically reaffirming the system prompt or shortening the dialogue history.

3. If in your case the role does not consistently affect decision-making, but you really want it to — then it’s worth relying on explicit instructions.

4. The link between “role — behavior” is unpredictable and manifests differently in different tasks and contexts. If behavior is important, and it’s difficult to formulate explicit instructions, don’t just rely on the prompt. Test the assigned “personality” in critical scenarios for you and in edge cases.

If the topic seems interesting to you, I continue to explore similar things in my Telegram with short posts, experiments, and examples from practice: “we need to figure it out | making LLM work”.