To integrate retrieval into multi-turn conversations effectively, the prompt must balance new context with relevant history. This requires structuring the input to highlight the most recent information while preserving key details from earlier interactions. The goal is to avoid overwhelming the model with redundant or outdated data while ensuring it has enough context to maintain coherence and accuracy. A common approach involves dynamically filtering or summarizing conversation history and explicitly separating new queries or retrieved content for clarity.
One practical method is to use a sliding window or token-aware truncation to retain recent turns and critical historical points. For example, if a conversation spans 10 messages but the model’s token limit only allows five, the system might keep the first message (e.g., initial query) and the four most recent exchanges. This preserves the conversation’s intent while prioritizing newer context. Additionally, retrieved information (e.g., database results) can be injected into the prompt as a distinct block, prefixed with labels like "Retrieved Context:" to distinguish it from the dialogue history. This separation helps the model process external data without conflating it with user-assistant interactions.
Another strategy involves generating concise summaries of earlier conversation segments. For instance, after each turn, a summarization model could condense the dialogue into a brief paragraph, which is then included in subsequent prompts. This reduces token usage while retaining essential context. For example, in a troubleshooting scenario, the summary might note, "User reported connectivity issues, tried restarting the router, and confirmed the modem is online." When a new query arrives (e.g., "Now my VPN won’t connect"), the prompt combines this summary, the latest message, and retrieved VPN troubleshooting steps. Developers can also use explicit instructions in the prompt, such as "Refer to the conversation summary and provided documentation to address the current issue," to guide the model’s focus. By combining these techniques—selective history retention, clear context separation, and summarization—the prompt maintains relevance without sacrificing efficiency.