When comparing ChatGPT and Google Gemini for custom vocabularies, their performance largely hinges on the breadth and depth of their training data and effective prompt engineering. Both models, built on extensive general internet corpuses, can initially struggle with truly novel or highly niche terms without explicit definitions or context. ChatGPT, leveraging its vast but generalized knowledge, often relies on contextual inference and its large context window to accurately interpret new terms when examples are provided. Gemini, potentially benefiting from Google's extensive knowledge graph and structured data, might show a slight advantage in recognizing well-indexed proper nouns, brand names, or entities. For optimal performance with custom vocabularies, both platforms critically depend on the user supplying clear definitions, usage examples, or supporting documents within the conversation to guide their understanding. Neither model inherently possesses perfect recall for every specialized lexicon without such contextual support. More details: https://www.smpn1-pamekasan.sch.id/redirect/?alamat=https://4mama.com.ua/