  We propose a new paradigm for machine translation that is particularly useful
for no-resource languages (those without any publicly available bilingual or
monolingual corpora): LLM-RBMT (LLM-Assisted Rule Based Machine Translation).
Using the LLM-RBMT paradigm, we design the first language
education/revitalization-oriented machine translator for Owens Valley Paiute
(OVP), a critically endangered Indigenous American language for which there is
virtually no publicly available data. We present a detailed evaluation of the
translator's components: a rule-based sentence builder, an OVP to English
translator, and an English to OVP translator. We also discuss the potential of
the paradigm, its limitations, and the many avenues for future research that it
opens up.
