In the rapidly evolving field of Generative AI, Retrieval-Augmented Generation (RAG) has become a cornerstone for building accurate and reliable AI systems. One of the most challenging yet frequently asked questions in interviews is: How do you handle multi-turn conversations in RAG?
A multi-turn conversation involves a sequence of user queries where context from previous turns must be preserved. In a standard RAG pipeline, each query is treated independently, which can lead to loss of context. To handle this effectively, the following approaches are recommended:
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Contextual Compression: Use the conversation history to expand or clarify ambiguous user queries. For example, if a user asks "What is its capital?" after asking about a country, the system should infer that "its" refers to the country mentioned earlier and retrieve accordingly.
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Query Rewriting: Rewrite the user's latest query by incorporating relevant context from previous turns. This rewritten query is then used for retrieval, ensuring that the retriever gets a self-contained question.
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Memory Management: Maintain a sliding window of recent conversation history or a compressed summary. This memory can be appended to the system prompt or used to update the retrieval context dynamically.
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Structured Retrieval: Store past interactions as metadata or in a separate vector store, enabling the model to retrieve not only relevant documents but also previous conversation snippets when needed.
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Agentic RAG: For complex scenarios, use an agent that decides when to retrieve additional information, when to ask clarifying questions, or when to rely on existing context. This approach is particularly useful for long conversations with multiple subtopics.
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Evaluation: Use metrics like recall of key entities across turns and consistency of answers to gauge performance.
By implementing these strategies, RAG systems can maintain coherent and context-aware conversations, significantly improving user experience in applications like chatbots, virtual assistants, and customer support.
This topic is frequently tested in AI interviews, so mastering it can set you apart in your next role.