danlenton

We just initialize a random latent vector for each model, and then jointly train each of these unique latent vectors :)

Currently the motivation is mainly speed. For the really easy ones like "hey, how's it going?" or "sorry I didn't hear you, can you repeat?" you can easily send to Llama3 etc. Ofc you could do some clever caching or something, but training a custom router directly on the task to optimize the resultant performance metric doesn't require any manual engineering.

Still, I agree that routing in isolation is not thaaat useful in many LLM domains. I think the usefulness will increase when applying to multi-step agentic systems, and when combining with other optimizations such as end-to-end learning of the intermediate prompts (DSPy etc.)

Thanks again for diving deep, super helpful!

Interesting, do you have any hunch as to why this is? We've seen in more verticalized apps where the underlying model is hidden from the user (sales call agent, autopilot tool, support agent etc.) that trying to reach high quality on hard prompts and high speed on the remaining prompts makes routing an appealing option.

no down side

Thanks for sharing, will get this fixed now!

If you do test it out, feel free to ping me with any questions!

Thanks for weighing in. I'm sure for your setup right now, our router in it's current form would not be useful for you. This is the very first version, and the scope is therefore relatively limited.

On our roadmap, we plan to support:

- an API which returns the neural scores directly, enabling model selection and model-specific prompts to all be handled on the client side

- automatic learning of intermediate prompts for agentic multi-step systems, taking a similar view as DSPy, where all intermediate LLM calls and prompts are treated as latent variables in an optimizable end-to-end agentic system.

With these additions, the subtleties of the model + prompt relationship would be better respected.

I also believe that LLMs will become more robust to prompt subtleties over time. Also, some tasks are less sensitive to these minor subtleties you refer to.

For example, if you have a sales call agent, you might want to optimize UX for easy dialgoue prompts (so the person on the other end isn't left waiting), and take longer thinking about harder prompts requiring the full context of the call.

This is just an example, but my point is that not all LLM applications are the same. Some might be super sensitive to prompt subtleties, others might not be.

Thoughts?

duly noted!