In a RAG system, should the original question be repeated or rephrased in the prompt along with the retrieved text, and what effect might that have on the answer?

In a RAG (Retrieval-Augmented Generation) system, whether to repeat the original question or rephrase it alongside the retrieved text depends on the context and goal. Directly repeating the question ensures the model stays aligned with the user’s intent, especially if the retrieved content is complex or contains multiple topics. Rephrasing, however, can refine the focus or adapt the query to better match the retrieved information. Both approaches influence how the model interprets the context and generates answers, so the choice should depend on the trade-offs between clarity and flexibility.

Repeating the original question verbatim helps anchor the model’s response to the user’s exact wording, reducing ambiguity. For example, if a user asks, “What causes battery degradation in smartphones?” and the retrieved text includes technical terms like “lithium-ion aging,” restating the question ensures the model prioritizes causes directly tied to the query. This approach works well when the retrieved content is highly relevant but dense, as it signals to the model to filter out tangential details. However, repetition might limit the model’s ability to infer implicit needs. If the original question is vague, like “How do I fix this error?” repeating it without additional context could lead to a generic answer, even if the retrieved text contains specific solutions.

Rephrasing the question can improve alignment with the retrieved content or clarify intent. For instance, if the original query is “Explain gradient descent,” but the retrieved text focuses on “stochastic gradient descent,” rephrasing the prompt to “Explain stochastic gradient descent” tailors the answer to the available information. This is useful when the retrieval step surfaces more precise terminology or subtopics. However, rephrasing risks introducing bias or misinterpreting the user’s goal. Over-modifying the question might lead the model to overlook key aspects of the original query. For example, changing “How does HTTPS work?” to “Describe HTTPS encryption” might narrow the answer to security features while omitting broader concepts like handshake protocols.

The best approach depends on balancing fidelity to the user’s input with adaptability to the retrieved context. If the retrieved text directly answers the original question, repetition is safer. If the content is tangential or requires synthesis, a carefully rephrased prompt can yield more accurate results. Developers should test both strategies with real-world examples—like comparing answers for “What’s React?” (repeated) versus “Explain React’s component model” (rephrased)—to determine which method suits their system’s performance and user needs. Monitoring metrics like answer relevance and user feedback can guide optimization.