Few-shot examples in a RAG (Retrieval-Augmented Generation) system help guide the model to use retrieved information effectively by providing concrete demonstrations of how to process context and generate answers. In practice, this involves including one or more structured examples in the prompt, each containing a sample question, the relevant context retrieved, and a model-generated answer that correctly leverages that context. These examples act as templates, showing the model how to extract key details from the provided information and structure its response. For developers, this approach reduces ambiguity by clarifying the expected relationship between the retrieved data and the final output.
For instance, consider a RAG system designed to answer historical questions. A few-shot example might include a question like, “When did the Berlin Wall fall?” paired with a retrieved context snippet: “The Berlin Wall, a symbol of Cold War division, was opened on November 9, 1989, leading to Germany’s reunification.” The corresponding answer in the example would directly reference the date and event from the context: “The Berlin Wall fell on November 9, 1989, when East German authorities opened the borders.” By including this example, the model learns to prioritize specific dates and events from the context rather than relying on general knowledge or guesswork. Developers can extend this to other domains—like technical documentation or customer support—by tailoring examples to show how to extract steps, error codes, or product details from retrieved texts.
To implement this, structure the prompt with clear separators (e.g., "Question:", "Context:", “Answer:”) for each example to help the model distinguish between components. For example:
Question: What causes a '404 Not Found' error?
Context: HTTP 404 errors occur when a client requests a resource the server cannot locate.
Answer: A '404 Not Found' error happens when the server cannot find the requested resource.
Including 2-3 such examples covers diverse scenarios, reinforcing how to map context to answers. Developers should ensure examples are concise, relevant to the target use case, and free of unnecessary details. While this approach improves reliability, testing is still required to refine the model’s ability to handle edge cases or ambiguous contexts. By prioritizing clarity in examples, developers can train the model to consistently align its outputs with the provided evidence.
