To structure prompts for LLMs to effectively use retrieved context, start by clearly defining roles and expectations. Begin with a system message that explicitly instructs the model to prioritize the provided context, such as: "Use the following passages to answer the query. Base your response solely on this information." This sets the model’s behavior upfront, reducing the chance of ignoring or misinterpreting the context. For example, a system prompt like this tells the LLM to anchor its response to the given data, which is critical for tasks like fact-based Q&A or document analysis. Without this directive, the model might default to its internal knowledge, leading to irrelevant or outdated answers.
Next, format the context and query to ensure clarity. Place the context immediately after the system message and before the user’s question, using visual markers like --- or ### to separate sections. For instance:
System: Use the passages below to answer.
Passages: [Context here]
---
User: What caused the 2008 financial crisis?
This structure helps the LLM parse the input correctly. Avoid embedding the context within the question (e.g., "Answer using [context]..."), as this can fragment the model’s attention. Instead, present the context as a standalone block, which mimics how humans process reference materials before addressing a problem. Additionally, if the context is long, break it into concise chunks or bullet points to improve readability and focus.
Finally, include explicit instructions for handling uncertainty. If the context is incomplete or ambiguous, guide the LLM to acknowledge gaps rather than guess. For example: "If the passages don’t contain enough information, respond with 'Not enough context to answer.'" This reduces hallucinations and aligns the output with the provided data. You can also ask the model to cite specific sections of the context (e.g., "Reference paragraph 2 in your answer") to encourage precision. Testing variations of these elements—like adjusting the order of context and query or adding validation steps (e.g., "Confirm your answer is supported by the passages")—helps identify the most reliable structure for your use case.