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.

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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.

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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

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Continue reading “Designing Language-Safe AI Systems: Deterministic Guardrails for Multilingual Enterprise AI” →