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.

This episode explains the attention mechanism in Transformer architecture, a crucial component of large language models (LLMs). It breaks down the process into key steps: creating and updating word embeddings to reflect contextual meaning, and attention scores.

The explanation uses analogies and illustrations to clarify complex concepts. This episode also covers the encoder-decoder structure of Transformers and its variations.

Let's start with the basics!

What is Generative AI?

"Imagine having a super-smart art student who learned by looking at millions of paintings. After learning, they can create new artwork based on what you ask for. That's kind of what generative AI does! It learns from examples and then creates new stuff."

It's different from older computer programs because it learns patterns instead of following strict rules

It's like having a creative computer friend that can help make things

Listen to understand more!!