Examples of RAG Prompt Templates RAG (Retrieval-Augmented Generation) prompt templates structure how a model processes retrieved context and user queries. Two common formats are:
- Direct Q&A with Context:
Question: What causes climate change?
Context: [Retrieved documents about greenhouse gases, fossil fuels, etc.]
Answer:
This template explicitly separates the question, context, and answer, guiding the model to focus on the provided documents.
- Conversational Style:
Based on the context below, explain in simple terms: "How do greenhouse gases affect global warming?"
Context: [Retrieved documents]
This format mimics natural dialogue, encouraging the model to synthesize the context into a more fluid explanation.
Impact of Template Structure on Results The Q&A style tends to produce concise, fact-focused answers. For example, when asking about climate change, the model might directly list causes from the context (e.g., "CO2 emissions from burning fossil fuels"). However, it risks ignoring broader connections if the context is narrow. Conversely, conversational prompts often yield more detailed, explanatory responses. For instance, the model might analogize greenhouse gases to a "blanket trapping heat," making the answer more accessible but potentially introducing unsupported details if the context lacks depth.
Trade-offs and Use Cases The Q&A template is ideal for technical audiences needing precise, citation-style answers, as it minimizes hallucinations by tightly coupling the response to the context. However, it may sound robotic. Conversational templates are better for user-facing applications (e.g., chatbots), where readability matters. However, they increase the risk of the model "filling in gaps" beyond the context, especially if the retrieved data is incomplete. Choosing between them depends on the balance required between accuracy, brevity, and user engagement.