Generation with a Swarm Architecture

 

Optimizing Dialog LLM Chatbot Retrieval Augmented Generation with a Swarm Architecture

Retrieval augmented generation (RAG) has become a dominant paradigm for creating conversational AI agents like LLM chatbots.

By retrieving relevant information and context, RAG allows dialog models to go beyond their training data and have more natural, knowledgeable conversations.

However, as RAG scales to real-world production use, several challenges emerge.

In this article, I discuss how a swarm architecture can help optimize and solve some of these RAG challenges for dialog chatbots.

RAG combines a powerful neural dialog generator model like GPT-3 with the ability to retrieve and incorporate external knowledge and context.

At its core, RAG consists of two main components:

Retriever: Responsible for finding and retrieving relevant context for the current conversation from various sources like:

Generator: A large language model that incorporates the retrieved context and generates a response.

By providing relevant external information to the generator, RAG reduces hallucination and repetition while improving specificity and factual grounding compared to conversation without retrieval.

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