Understanding the Foundations of Large Language Models

In this video, we will dive into the basics of large language models (LLMs) pipeline. We'll explore how these models can do more than just predict the next word in a sentence - they can think, reason, and even philosophize.
We'll try to understand how LLMs are trained and why they're so powerful. Then, we'll take a look at the inner workings of LLMs, focusing on their architecture and how data preparation plays a crucial role in their performance. We'll also discuss about evaluation methods and techniques for enhancing model performance.
One exciting aspect we'll cover is retrieval-augmented generation (RAG), where LLMs use stored documents to generate responses.
We'll also touch on prompt engineering, which can further improve LLM performance, and the importance of guardrails in keeping these models on track and unbiased.
Finally, we'll briefly mention GenAI Apps - applications that leverage LLMs - and how you can explore them further.
This video aims to set the stage for practical exercises in upcoming labs. Ready to explore the world of large language models? Let's dive in!