[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-rephrase-and-respond::en":3,"gloss-cluster-rephrase-and-respond::en":23,"gloss-next-rephrase-and-respond::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"rephrase-and-respond","prompt-eng","Rephrase and Respond (RaR)","Rephrase and Respond (RaR) is a simple prompting technique where you ask the model to restate and expand the question in its own words before answering it. Introduced by Deng et al. (2023), the insight is that human phrasing is often ambiguous, underspecified, or full of assumptions the model would misread — so letting it first rewrite the question into a clearer, more explicit form reduces silent misunderstandings and improves the answer. It can be one step ('Rephrase the question, add any needed detail, then answer') or two prompts, where a stronger model rewrites and another responds. It's cheap, model-agnostic, and stacks with Chain-of-Thought. For builders, it's most useful on terse user inputs — search boxes, support tickets, one-line commands — where intent is easy to misjudge. A bonus: the rephrased question is a useful debugging signal, showing exactly how the model interpreted the request. Caveat: on already-clear, well-scoped prompts it mainly adds tokens and latency for little gain, so reserve it for genuinely messy input.","Rephrase and Respond asks the model to restate and expand a question in its own words before answering — cheap insurance against ambiguous human phrasing.",null,[11,14,17,20],{"slug":12,"name":13},"chain-of-thought-prompting","Chain-of-Thought Prompting",{"slug":15,"name":16},"self-ask-prompting","Self-Ask Prompting",{"slug":18,"name":19},"step-back-prompting","Step-Back Prompting",{"slug":21,"name":22},"zero-shot-prompting","Zero-Shot Prompting",[24,28,31,34,36,39,42,45,48,51,54,57],{"slug":25,"category":5,"name":26,"updated_at":27},"analogical-prompting","Analogical Prompting","2026-08-24T02:46:37+00:00",{"slug":29,"category":5,"name":30,"updated_at":27},"automatic-prompt-optimization","Automatic Prompt Optimization",{"slug":32,"category":5,"name":33,"updated_at":27},"chain-of-density","Chain of Density (CoD)",{"slug":12,"category":5,"name":13,"updated_at":35},"2026-08-24T02:46:36+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"chain-of-verification","Chain-of-Verification",{"slug":40,"category":5,"name":41,"updated_at":35},"chunking","Chunking",{"slug":43,"category":5,"name":44,"updated_at":35},"constrained-decoding","Constrained Decoding",{"slug":46,"category":5,"name":47,"updated_at":35},"context-stuffing","Context Stuffing",{"slug":49,"category":5,"name":50,"updated_at":35},"delimiter","Delimiter",{"slug":52,"category":5,"name":53,"updated_at":27},"directional-stimulus-prompting","Directional Stimulus Prompting",{"slug":55,"category":5,"name":56,"updated_at":27},"emotion-prompting","Emotion Prompting",{"slug":58,"category":5,"name":59,"updated_at":35},"few-shot-prompting","Few-Shot Prompting"]