Routing with LLM-Based Classifiers for Complex Tasks

In LLM applications, handling diverse user queries efficiently is crucial. Traditional approaches often involve hardcoded routing rules, which can become inflexible and require manual maintenance as the system grows. LLM-based classifiers offer a dynamic and adaptable solution.

Some of the benefits include:

• Automatic classification: LLMs can analyze query content to automatically classify it into relevant categories, reducing manual configuration.

• Scalability: The system can learn new categories and adapt to evolving query patterns without code changes.

• Improved user experience: Precise classification ensures users receive responses from specialized LLMs, leading to more accurate and relevant answers.

What is routing with LLM-based classifiers?

It’s a technique that leverages an LLM to categorize user queries into predefined domains or topics. This classification then routes the query to the most appropriate LLM for generating a response. Here’s a breakdown of how it works:

• Query submission: The user submits a query.

• LLM-based classification: An LLM trained on a dataset of labeled queries analyzes the user’s query and predicts its category (e.g., personal finance, book review, health & fitness, travel guide).

• Routing: Based on the ...