Routing with LLM-Based Classifier
Learn about routing with LLM-based classifier and its step-by-step implementation.
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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 predicted category, the system routes the query to the corresponding LLM specializing in that domain.
LLM response generation: The specialized LLM processes the query and generates a tailored response.
Output delivery: The user receives the response from the appropriate LLM.
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