Interactive RAG: Conversational Retrieval for Live Assistants

Interactive RAG runs retrieval each turn in a conversation and adapts with user feedback to clarify and refine answers.

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