Routing: An Underappreciated Component of RAG and AI Chatbots

At Mira, we've been deeply involved in developing Retrieval-Augmented Generation (RAG) systems and Large Language Model (LLM) applications. Through this process, we've come to realize that routing is a crucial aspect of these systems that often doesn't receive the attention it deserves.

What is Routing in RAG and AI Chatbots?

Routing in the context of RAG and AI chatbots refers to the process of directing user queries or inputs to the most appropriate subsystem or knowledge base. It's essentially a classification step that determines how to handle each user interaction.

Why is Routing Important?

1. Efficiency: Proper routing ensures that queries are handled by the most relevant part of your system, reducing unnecessary processing and improving response times.
2. Accuracy: By directing queries to specialized subsystems, you can provide more accurate and relevant responses.
3. Resource Management: Effective routing helps in managing computational resources by only engaging necessary components for each query.
4. User Experience: Quick and accurate responses, facilitated by good routing, lead to a better user experience.

The Pitfalls of Not Using Routing

Without a proper routing system, RAG and AI chatbot applications can face several challenges:

1. Increased Latency: Without routing, the system may need to search through all available data for every query, leading to slower response times.
2. Reduced Accuracy: Generic responses that don't leverage specialized knowledge bases can be less accurate or relevant.
3. Higher Costs: Processing every query through the entire system can lead to unnecessary computational costs.
4. Inconsistent User Experience: Without routing, the quality and relevance of responses may vary widely, leading to an inconsistent user experience.
5. Scalability Issues: As the knowledge base grows, the lack of routing can make it increasingly difficult to maintain performance and accuracy.

Approach

At Mira, we've implemented a classification-based routing system. Here's a simplified version of our approach:

1. We use llm to classify each user message into predefined categories.
2. Based on the classification, we route the query to the appropriate subsystem or knowledge base.
3. Our categories are already defined and we use the classification model to classify the user query into one of the categories.

This approach allows us to handle a wide range of queries efficiently and accurately.

Challenges and Future Directions

While our current routing system is effective, we're continually working on improvements. Some areas we're exploring include:

• Fine-tuning the classification model for better accuracy
• Expanding our categories to handle a broader range of queries