How do I get HuggingChat to stick to a brand voice guide? It keeps drifting.

I've been conducting a thorough evaluation of HuggingChat for potential deployment as a first-tier customer support chatbot, with the intent of routing qualified queries to our human agents. A core requirement for this use case is the ability to maintain a consistent brand voice—specifically, we need a tone that is consistently helpful, slightly formal yet approachable, and avoids colloquialisms or excessive enthusiasm. My experiments have revealed a persistent issue: the model exhibits significant "voice drift" over the course of a conversation or even within a single, moderately complex response.

I can provide an initial system prompt that establishes guidelines (e.g., "You are a support assistant for [Brand]. Use professional language, avoid slang, and focus on actionable steps. Do not use phrases like 'Awesome!' or 'No worries.'"). For the first few exchanges, compliance is often satisfactory. However, upon deeper probing or when the user query requires a multi-part explanation, the model frequently reverts to a more generic, informally cheerful assistant persona. This is problematic for brand integrity and creates an inconsistent customer experience.

My testing methodology has involved side-by-side comparisons with other platforms that offer more granular "personality" or "guardrail" controls. In the absence of such native features within HuggingChat's interface, I have attempted to mitigate the drift through:

* **Prompt Engineering:** Strengthening the initial instruction with explicit negative examples and positive templates. This improves initial alignment but decay is still observed.
* **Context Management:** Periodically re-injecting the core voice guideline within the conversation history. This is operationally cumbersome and fragments the dialogue flow.
* **Parameter Tuning:** Adjusting temperature and top_p settings to reduce randomness, which helps with consistency but can make responses feel rigid or repetitive.

My primary questions for the community are thus:

* Has anyone successfully implemented a stable brand voice using HuggingChat in a production-like environment, particularly for customer-facing functions?
* Are there specific prompting architectures or meta-prompts that have proven more resilient against this type of drift?
* Given that fine-tuning the underlying model is likely beyond the scope of many teams, are there any inference-time techniques or external middleware approaches that could enforce style consistency?
* How does this challenge compare, in your experience, to using dedicated customer support AI platforms that often have brand voice as a built-in configuration module?

I am particularly interested in any structured analyses of the trade-offs between using a general model like HuggingChat with extensive prompt engineering versus a specialized solution with baked-in tonal controls, especially when considering total cost of ownership and maintenance overhead.