Getting started with LLMs
Throughout this chapter, we will cover components and terminologies around LLMs and concepts that are crucial for data modeling for LLM-based applications. However, the detailed architecture involved in creating LLM-based applications is outside the scope of this chapter. Here is an overview of the architecture and functioning of LLMs, which are typically composed of three main components:
- Encoder: The encoder is responsible for converting the input text into a sequence of numbers. This is done by representing each word in the input text as a vector of numbers.
- Decoder: The decoder is responsible for generating the output text from the sequence of numbers. This is done by predicting the next word in the output text, given the previous words.
- Transformer: The transformer is a neural network that is used to train the encoder and decoder. It can learn long-range dependencies between words.
To give a high-level summary, LLMs work by learning...
