What are effective ways to structure the prompt for an LLM so that it makes the best use of the retrieved context (for example, including a system message that says “use the following passages to answer”)?

Effective Prompt Structuring for Leveraging Retrieved Context in LLMs To ensure an LLM effectively uses retrieved context, structure prompts with explicit instructions, clear context separation, and task-specific guidance. Start with a system message like “Use the following passages to answer the query” to prime the model to prioritize the provided information. Place the context immediately after this directive, using visual markers (e.g., --- or XML tags) to distinguish it from the query. For example:

System: Use the passages below to answer. 
Context: [Retrieved passages here] 
Question: [User query] 

This approach reduces ambiguity, ensuring the model recognizes the context as the primary source. Developers should avoid burying the context within the query or using vague language like “refer to this information,” which can lead the model to ignore or underutilize the provided data.

Optimizing Context Presentation and Relevance The order, length, and relevance of the retrieved context significantly impact performance. Place the most critical information first, as LLMs process text sequentially and may assign higher weight to earlier content. For instance, if answering a technical question about API design, start with the most relevant documentation snippet. Trim extraneous details to avoid overwhelming the model—context exceeding 500 tokens often degrades accuracy. Use formatting like bullet points or numbered lists to improve readability. Example:

Context: 
1. API authentication requires OAuth 2.0 tokens. 
2. Rate limits: 100 requests/minute. 
3. Error code 429 indicates rate limit exceeded. 
Question: How to handle error 429 in the API? 

This structure helps the model quickly identify key details. If the context is irrelevant, explicitly state, “If the passages don’t answer the question, say ‘No relevant information found,’” to prevent hallucination.

Handling Edge Cases and Iterative Refinement Even well-structured prompts can fail if the context is incomplete or contradictory. Address this by adding fallback mechanisms. For example, include a final instruction like, “If the passages conflict, explain the ambiguity.” For complex tasks, break the query into sub-questions and map each to specific context sections. Developers should test prompts iteratively: Start with a simple structure, evaluate outputs, and refine based on failure modes. For instance, if the model ignores a critical passage, add emphasis with phrases like “Focus especially on [key detail].” Tools like LangChain’s “RetrievalQA” chain automate parts of this process, but manual tuning remains essential for niche use cases.

By combining explicit instructions, context prioritization, and iterative testing, developers can significantly improve an LLM’s ability to leverage retrieved information accurately and efficiently.