How do you implement RAG for conversational AI with multi-turn context?
Multi-turn RAG maintains conversation context while retrieving fresh information for each turn. Challenges: the current query may reference previous turns (anaphora resolution: "What about that one?" needs context to understand), conversation topic may drift (retrieval must track topic changes), and context window fills up with conversation history plus retrieved chunks. Query reformulation: before retrieval, rewrite the current query to be self-contained by incorporating relevant context from conversation history. Implementation: use an LLM to generate a standalone query (e.g., user says "How does it handle errors?" after discussing Temporal, reformulated to "How does Temporal handle errors in workflow execution"). Retrieval strategy: retrieve based on reformulated query, optionally weight recent conversation topics higher, and implement conversation-aware reranking that considers topic continuity. Context management: implement a sliding window over conversation history (keep last N turns), summarize older turns to compress context, prioritize recent retrieved chunks while maintaining relevant older context, and implement a context budget: conversationhistory (30%) + retrievedchunks (50%) + systemprompt (10%) + generationbuffer (10%). Memory architecture: short-term memory (current conversation turns), working memory (retrieved context relevant to current query), and long-term memory (user preferences, previous conversations stored in a database for personalization). Implementation: store conversation history in a session store, index important conversation facts for future retrieval, and implement conversation summary generation at configurable intervals. Evaluation: measure multi-turn coherence (does the system maintain context), retrieval relevance per turn, and answer quality considering full conversation context.