Whether you're building an experimental prototype for your own personal use, or creating an application to power an entire organization, there are key components of the AI technology stack that you must get right to build AI systems that can do more than just generate answers but solve real, meaningful problems.
Say, for instance, I'm building an AI-powered application to help drug discovery researchers understand and analyze the latest scientific papers in their domain.
Maybe it starts with a model that I recently heard about that is supposed to be better at highly complex tasks like that of a PhD researcher.
Model is an important layer of the stack, but it's just one piece of the puzzle.
There's also the infrastructure that that model will run on, because not all LLMs, large language models, can run on standard enterprise CPU-based servers, and not all are small enough to run on a laptop.
So it matters what infrastructure you have access to and how you choose to deploy it.
Next is data because in this example, the whole point is to help scientists understand the latest papers in their field.
And models typically have a knowledge cutoff date.
So if we want to talk about papers from, say, the past three months, that means we have to provide the AI system with extra data.
That will be the data layer.
