Few-shot examples in RAG (Retrieval-Augmented Generation) prompts guide the model to integrate retrieved context into answers by providing explicit demonstrations of the desired input-output pattern. Each example typically includes a question, relevant context snippets, and a model-generated answer that directly references the provided context. By structuring these examples clearly, the model learns to prioritize retrieved information, synthesize it, and avoid hallucination. For instance, an example might show that when answering a question about a historical event, the model should first identify key details from the context (e.g., dates, causes) and then structure the answer around those facts. This approach reduces ambiguity about how to use external knowledge and sets expectations for answer quality.
A practical implementation might format examples as follows:
- Question: "What caused the decline of the Roman Empire?" Context: "Economic instability, military overexpansion, and invasions by Germanic tribes contributed to the fall." Answer: "The Roman Empire declined due to economic crises, excessive military spending, and invasions from groups like the Visigoths (as noted in the context)."
- Question: "How does SSL encryption work?" Context: "SSL uses asymmetric cryptography to establish a secure session and symmetric keys for data transfer." Answer: "SSL first negotiates a secure connection using public-private key pairs, then switches to faster symmetric encryption for data transmission (based on the provided context)."
These examples train the model to anchor answers to specific context elements, use attribution phrases like "as noted in the context," and avoid introducing unsupported claims. Including multiple examples with varied question types (factual, explanatory, comparative) helps the model generalize the pattern.
Key considerations include balancing example specificity with flexibility. Overly rigid templates (e.g., always starting with "According to the context...") may limit creativity, while too little structure risks inconsistent context usage. Examples should reflect real-world retrieval scenarios, including partial or conflicting context. For technical domains, adding code snippets or structured data in examples can demonstrate how to parse technical documents. Testing with edge cases (e.g., context that lacks a direct answer) helps refine the model's ability to handle uncertainty while maintaining the demonstrated reasoning pattern.