Cis correct — LLMs like those used in UiPath’s Agentic Automation rely heavily ontokenization, which breaks input text into subword units (tokens). When complex technical terms (e.g., “UiPath.Orchestrator.API”) aresplit across multiple tokens, the model may not interpret themconsistently or accurately, especially if:
They're rare or domain-specific
Appear in different token contexts
Are inconsistently represented in training data
This is a common challenge in fine-tuning LLMs fortechnical documentation, where small changes in tokenization can shift meaning or relevance weighting. It’s why UiPath emphasizesprompt engineeringandcontext groundingto mitigate misinterpretation.
A is incorrect because thetoken limitaffects response length, not term understanding.
B is misleading — frequency matters, butsemantic relationshipsalso influence interpretation.
D is factually wrong — LLMs absolutely rely on tokenization and arenot rule-basedwith pre-programmed definitions.
Understanding how tokenization impacts prompt fidelity is critical when building agents that use LLMs to generatestep-by-step or technical outputs.
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