Are LLMs the new IP layer in need of TCP?

Ever wonder how the internet keeps everything flowing smoothly? It’s all about layers working together. At the heart of it, there’s the network layer (layer 3), like IP, which handles routing data from point A to point B. But it doesn’t work alone — it relies on the transport layer (layer 4), like TCP, to manage connections, retries, and ensure everything arrives intact.

Now, let’s talk about Large Language Models (LLMs). They’re becoming the backbone of many systems, kind of like the new network layer in our tech world. LLMs process and generate human-like text, enabling machines to understand and communicate with us more naturally.

Working with the unpredictability of LLMs in code feels very similar to network programming. You often need to implement retries, apply backoff strategies, and occasionally add transaction identification to ensure consistency. I wouldn’t be surprised if this evolves into a formal layer.

But here’s the thing: while LLMs are powerful, they don’t inherently handle things like failovers, retries, or efficient routing of tasks. Just as IP needs TCP to ensure data gets where it needs to go reliably, LLMs need an equivalent “transport layer” to manage these operational details.

Imagine interacting with an AI assistant, and suddenly it goes blank because of a hiccup. Without a system to handle retries or reroute the request, the experience falls flat. A dedicated layer on top of LLMs could manage these issues — handling errors gracefully, retrying failed operations, and maintaining smooth communication.

Developing this “LLM transport layer” would make interactions more reliable. It could monitor the health of different LLM instances, route requests to the best-performing ones, and manage the flow of information to prevent bottlenecks.

In essence, while LLMs are revolutionizing how we interact with technology, they need that extra layer to truly shine in complex, real-world systems. By building protocols and tools that handle the nitty-gritty — like retries and failovers — we can ensure that the AI systems of the future are not just smart, but also robust and dependable.

So, could this be the next big step in AI development? Crafting a transport layer for LLMs might just be what we need to take full advantage of their capabilities, ensuring smooth and reliable interactions in our increasingly AI-driven world.