Imagine having a coding agent that can dynamically understand your tasks and route them to the most suitable model for the job. Sounds like a dream, right? Well, a Reddit user, /u/AdditionalWeb107, has made this a reality by building a specialized LLM that decouples route selection from model assignment.
The idea is to split the routing process into two distinct parts: route selection and model assignment. The router model uses a ‘Domain-Action Taxonomy’ to match the user’s query to the best-fit policy description. This policy description is then mapped to a specific LLM, which can be a powerful model like GPT-4 or a simpler, faster model. This approach eliminates the need to incur a heavy cost on first prompting a foundational model to determine the optimal model.
The benefits of this approach are twofold. Firstly, it reduces the latency and token cost associated with using a foundational model. Secondly, it provides a better user experience by allowing developers to define their preferred models for specific tasks. The router model is not limited to coding tasks and can be used to define route policies for image editing, creative writing, and more.
The researcher has made the full research paper and detailed links available in the comments section. If you’re interested in trying out this innovative solution, be sure to check it out and provide your feedback.