Large Language Models are exceptionally good at producing fluent text.
They are not inherently good at knowing when not to answer.
In regulated or compliance-sensitive environments, this distinction is critical.
A linguistically plausible answer that is not grounded in official documentation is often worse than no answer at all.
This article describes a practical architecture for handling language detection, translation intent, and multilingual retrieval in an enterprise AI system — with a strong emphasis on determinism, evidence-first behavior, and hallucination prevention.
The examples are intentionally domain-neutral, but the patterns apply to legal, regulatory, financial, and policy-driven systems.
The Core Problem
Consider these seemingly simple user questions:
"What is E104?"
"Slovenski prevod za E104?"
"Hrvatski prevod za E104?"
"Kaj je E104 v slovaščini?"
"Slovenski prevod za Curcumin?"At first glance, these look like:
- definitions
- translations
- or simple multilingual queries
A naïve LLM-only approach will happily generate answers for all of them.
But in a regulated environment, each of these questions carries a different risk profile:
- Some require retrieval
- Some require translation
- Some require terminology resolution
- Some should result in a deterministic refusal
The challenge is not generating text —
it is deciding which answers are allowed to exist.
Key Design Principle: Evidence Before Language
The system described here follows one non-negotiable rule:
Language is applied after evidence is proven, never before.
This means:
- Language preference never expands the answer space
- Translation never invents facts
- Missing official wording is explicitly acknowledged
Continue reading “Designing Language-Safe AI Systems: Deterministic Guardrails for Multilingual Enterprise AI”
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