Summary

In this chapter, we covered what ChatGPT is and what LLMs in general are, the origins of some widely used LLMs such as BERT, the GPT family, LlaMDA, LlaMA, and modern LLMs such as GPT-4 and Gemini. We looked at some architecture of LLMs and transformers. We had a go at fully processing a sentence in the way an LLM model would: tokenizing, Word2Vec contextual embedding, and more. We also touched on the types of mathematics involved and the applications of this fantastic technology deployed by companies.

Hopefully, you now understand the nature of LLMs such as ChatGPT/Gemini; understand the architectures of LLMs; understand some mathematics of LLMs; and are enlightened about competition in the field and how to teach LLMs to others.

In Chapter 2, we will look at the advantages of coding with LLMs, planning your LLM-powered coding, doing some coding with LLMs, and making it work for you.