A user on Reddit asked a simple question that hits a nerve for anyone working with language models: how do you remove trendy language from LLMs? The examples are concrete: instead of saying 'I created this new ID', the model writes 'I minted this new ID'; instead of 'an alternative path is available', it produces 'this escape hatch is available'. The annoyance is legitimate, but the issue goes beyond personal taste.

The phenomenon is not random. LLMs learn from the texts they are trained on. After the surge in popularity of these models, a growing portion of the online corpus has been produced by LLMs themselves or by people imitating their style. Terms like 'mint' or 'escape hatch' have become frequent in tech contexts, and models have absorbed them as preferred patterns. It's a feedback loop: the more trendy language enters the data, the more models reproduce it.

The real implication for on-premise deployments is not aesthetic but about control. A company using a self-hosted LLM to generate documentation or customer responses needs predictable and uniform output. If the model chooses slang metaphors instead of literal terms, the risk is not just sounding unprofessional: it's introducing semantic ambiguity that can be costly in regulated or technical contexts. The most immediate solution suggested by the user — adding a system instruction like 'don't use X language, speak normally and literally' — can reduce the problem but does not eliminate it structurally.

System instructions act on surface behavior, but they do not change the probabilities learned during training. For stable results, deeper interventions are needed: fine-tuning on a corporate corpus that excludes unwanted jargon, post-generation filters, or even choosing models trained with more traditional data. The trade-off, however, is between control and computational cost: fine-tuning requires resources and skills, while post-hoc filters can introduce latency. In an on-premise context, where the company owns the model weights, these options are feasible; in the cloud, one is often limited to prompt instructions.

For those evaluating on-premise deployment, there are trade-offs between model behavior control and operational complexity: AI-RADAR offers analytical frameworks on /llm-onpremise to assess these aspects without prescribing a single solution.

The user's question is ultimately a warning about the quality of training data. If LLMs continue to feed on texts generated by other LLMs, the risk is a flattening of language toward slang patterns. Those who manage models in-house have an advantage: they can directly observe behavior and intervene on data. But the responsibility to maintain clear language is not only technical: it is a design choice.