LLM routing explained with 3 examples, simple to advanced

The video explains the concept of routing with Large Language Models (LLMS) through three examples, ranging from simple to advanced scenarios. It demonstrates how user queries can be directed to specific LLMS models based on context, showcasing the versatility and practical application of routing in various interactions, from casual conversations to customer service scenarios.

In the video, the concept of routing with LLMS (Large Language Models) is explored through three examples ranging from simple to advanced scenarios. The first example demonstrates how user queries can be routed to specific LLMS based on the context of the query. For instance, complex questions are directed to GP4 Omni, code-related queries to 3.5 Sonet, and simple daily conversations to Lama 38 billion. This initial example showcases the basic functionality of routing user queries to different LLMS models in real-time during a single conversation.

Moving on to the second, more advanced example, the video shows how routing to different LLMS models can be combined with multiple system messages for each model. For instance, users routed to Lama 38 billion for casual conversations are met with a friendly conversational assistant, while those with code-related queries to Cloud 3.5 Sonet are greeted by an expert software engineer or data scientist. This example demonstrates how routing can be customized with specific system messages tailored to different user interactions.

The video also delves into a customer service routing scenario where user queries are directed to different departments such as Electronics, Fashion, Home Garden, or Books/Media. Each department has specific system messages associated with it, providing users with tailored responses based on their query. This example showcases how routing can be used in practical applications such as customer service interactions to efficiently direct users to the appropriate department.

The implementation of the routing system is detailed, showing how OpenAI with GPT-4 in JSON mode is utilized for routing and how Open Router models are initialized for the routing process. The video explains the process of determining which LLMS model to route the user query to based on predefined criteria and system messages. The code review highlights the simplicity of the routing system and how it can be expanded and customized for various use cases.

In conclusion, the video emphasizes the versatility and creative potential of routing with LLMS, showcasing how it can be applied in diverse scenarios from basic user queries to complex customer service interactions. Viewers are encouraged to explore the code examples provided in the video, with the first file available for free and the subsequent files accessible to Conosur Plus patrons. The video also highlights the benefits of becoming a patron to access code files, courses, and engage with the content creator for further learning and collaboration opportunities.