AI BriefingConversational AI Powered by RAG
Key point
This introduces a method for building conversational RAG that combines multi-turn dialogue with dynamic planning to enhance the reliability of enterprise GenAI.
Details
The core of enterprise GenAI applications lies in the factuality and quality of their outputs. RAG (Retrieval-Augmented Generation) is one of the most valuable strategies for answering queries based on a company's private data.
To go beyond simple single-turn Q&A and achieve scalability, RAG must be combined with multi-turn conversation capabilities and dynamic planning strategies. This enables the implementation of a reliable work partner, such as an internal research tool or a customer-facing chatbot.
Conversational RAG adopts a compound AI system architecture. The Execution Engine evaluates incoming queries and decides whether to answer using the LLM's own knowledge or to retrieve information through the RAG Engine.
AI21 provides a fully managed service for this. When a user uploads a document in PDF, TXT, DOCX, or markdown format, the system automatically performs indexing in the optimal way. The article explains this process using an example of building a financial analysis assistant using Amazon's 10-K report.
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