#prompting

機械支援の翻訳下書き (Japanese) for "Prompt Engineering": The craft of designing, structuring, and refining inputs (prompts) to elicit desired outputs from large language models. A skilled prompt engineer understands how to use context, examples, formatting, and instruction clarity to guide model behavior without changing the underlying weights.

“例文の下書き: Good prompt engineering turned an unreliable prototype into a production-ready feature in just a week.”

機械支援の翻訳下書き (Japanese) for "Chain of Thought": A prompting technique where a language model is encouraged or required to show its step-by-step reasoning before providing a final answer. Chain-of-thought prompting significantly improves accuracy on complex tasks like math, logic puzzles, and multi-step planning.

“例文の下書き: Adding "let's think step by step" to the prompt triggered chain-of-thought reasoning and doubled accuracy.”

機械支援の翻訳下書き (Japanese) for "Zero-Shot": The ability of a model to perform a task it has never been explicitly trained or shown examples for. Zero-shot learning relies on the model's generalized understanding from pretraining to handle novel tasks based on instruction alone.

“例文の下書き: The model classified customer sentiment zero-shot without any labeled training examples.”

機械支援の翻訳下書き (Japanese) for "Few-Shot": A prompting approach where a small number of input-output examples are included in the context to guide model behavior on a new task. Few-shot prompting helps models understand the desired format, tone, or logic without any weight updates.

“例文の下書き: We gave the model three few-shot examples of our data format and it immediately understood the pattern.”