To integrate retrieval into multi-turn conversations effectively, the prompt must balance new context with relevant conversation history. This is typically achieved by structuring the prompt to include both a summarized or truncated history and the retrieved information. The key is to ensure the model has access to the most critical parts of the conversation while incorporating fresh data from the retrieval system. For example, the prompt might begin with a condensed version of the last few user-assistant exchanges, followed by the newly retrieved documents or facts. Developers often use a sliding window approach to retain recent messages (e.g., the last three turns) or apply text summarization to compress older interactions. This prevents exceeding token limits while preserving context.
Retrieval should be guided by the conversation’s current state and its history. When a user asks a follow-up question, the system must retrieve information that addresses both the immediate query and prior context. For instance, if a user first discusses “Python error handling” and later asks, "How do I log these errors?", the retrieval component should fetch documents related to logging libraries and error handling in Python. The prompt might then format this as: "[History] User: How to handle exceptions in Python? Assistant: Use try-except blocks. [Retrieved Context] Python’s logging module helps track errors. [Current Query] User: How do I log these errors?". This ties the new information to the existing thread, ensuring coherence.
To maintain relevance, developers must filter retrieved content to avoid redundancy or off-topic results. Techniques like similarity scoring between the query and retrieved documents, or entity tracking (e.g., identifying recurring terms like “logging” or “exceptions”), help prioritize useful context. For example, if a conversation shifts from “API authentication” to “rate limits,” the system should retrieve rate-limiting docs but retain key authentication terms like “OAuth tokens” in the prompt. Tools like vector databases can compare embeddings of historical turns against new queries to surface related context. By dynamically adjusting the blend of history and retrieval, the system stays focused without losing critical details from earlier interactions.
