Interactive RAG: Conversational Retrieval for Live Assistants

TL;DR

Interactive RAG integrates retrieval into multi-turn conversations, preserving context and letting users steer follow-ups for better answers.

What Is Interactive RAG?

A conversational setting where each user message triggers retrieval over a KB and chat history, then generation with updated context.

When to Use Interactive RAG

• Customer support assistants over internal KBs
• Tutoring systems enabling clarifying questions
• Any app needing live user feedback loops

Building Interactive RAG in N8N

• Trigger on each user message
• Retrieve from memory (chat turns) and static corpus
• Generate response and detect if user wants more detail
• Branch using If/Switch nodes to fetch more and continue

Strengths & Weaknesses

Strengths: adjusts on the fly, captures corrections, feels assistant-like. Weaknesses: context management is hard, long chats increase token cost, evaluation is trickier.

Metrics to Track

• Dialogue relevance and satisfaction
• Turn-level accuracy and retention of context
• Latency per turn and overall cost