What foundational concepts should you study if you want to understand Large Language Models?
Most of the code we use to interact with LLMs (Large Language Models) is hidden behind several APIs — and that’s a good thing.
But if you are like me, and want to understand the ins and outs of these magical models, there’s still hope for you. Currently, apart from the researchers working on developing and training new LLMs, there’s mostly two types of people playing with these types of models:
- Users, that interact via applications such as ChatGPT or Gemini.
- Data scientists and developers that work with different libraries, such as llangchain, llama-index or even using Gemini or OpenAI apis, that simplify the process of building on top of these models.
The problem is — and you may have felt it — that there is a fundamental knowledge in text mining and natural language processing that is completely hidden away in consumer products or APIs. And don’t take me wrong — they are great for developing cool use cases around these technologies. But, if you want to a have deeper knowledge to build complex use cases or manipulate LLMs a bit better, you’ll need to check the fundamentals — particularly when the models behave as you…
