This episode explains large language models (LLMs) and details their features, such as being large, general-purpose, pre-trained, and fine-tunable. It then discusses various prompting techniques used to interact with LLMs, including zero-shot, few-shot, chain-of-thought, ReAct, and Tree of Thoughts prompting, highlighting their applications and advantages.

The episode also notes the continuous evolution of LLMs, with newer models often surpassing the capabilities of their predecessors.

Finally, this episode discusses examples of different LLM types and their uses.