In a RAG (Retrieval-Augmented Generation) system, including the original question verbatim in the prompt alongside retrieved text is generally preferable to rephrasing it. This approach ensures the model retains the exact intent and wording of the user’s query, which is critical for aligning the generated answer with the retrieved context. Rephrasing risks altering nuances or introducing ambiguity, especially if the restatement misinterprets the original question. For example, if a user asks, “How do neural networks learn?” rephrasing to “Explain the training process of AI models” might broaden the scope beyond the retrieved documents focused on neural networks, leading to a less precise answer.
Repeating the original question directly reinforces consistency between the retrieval and generation stages. The retrieved documents are typically ranked based on their relevance to the original query, so restating the question verbatim helps the generator model focus on the same keywords and concepts present in the context. For instance, if the question is “What causes climate change?” and the retrieved text includes terms like “greenhouse gases” and “carbon emissions,” retaining the exact phrasing ensures the model prioritizes these concepts. Conversely, rephrasing to “Why is the Earth getting warmer?” might lead the model to overlook contextually relevant terms, even if the intent is similar.
The primary effect of repeating the question is improved answer accuracy and relevance, as the generator model operates with a clear, unambiguous reference to the user’s goal. Rephrasing might occasionally help if the original question is poorly worded or overly vague, but this requires careful manual tuning to avoid mismatches. For most implementations, sticking to the original question reduces risks and aligns with the RAG architecture’s design, where retrieval and generation are tightly coupled to the input query. Developers should prioritize verbatim repetition unless specific edge cases (e.g., clarifying ambiguous queries) warrant controlled rephrasing.