A Primer on How to Deploy LLMs: Part 1 - by theahura
This document series aims to build a basic intuition for how LLMs can be used to solve a wide range of tasks. This series does not cover code except to provide examples. You won't really be able to actually implement an LLM using this series alone. Rather, this describes concepts. By the end of this series, you should be able to reason about how you can deploy LLMs for a wide range of tasks (and then go to a service like https://together.ai/ or OpenAI to do the rest).
For the purposes of this document, we're going to abstract away what an LLM actually is. Whenever you see LLM, I want you to think about the following image:
Like any other abstracted black box, we need to describe the properties of this thing. How, exactly, does it turn one string into the other?
To a first approximation, the instructions for how the black box should work are present in the input string itself. So whatever you want the LLM to do needs to be present in the input string. As such, it can be useful to model the input as containing 'instructions' and 'data'.
