How I use LLMs

What You Will Learn

• How to use Large Language Models (LLMs) for practical applications
• How to choose the right model for a specific task
• How to use tools like internet search and programming languages with LLMs

Key Concepts

• LLMs are trained in two stages: pre-training and post-training. Pre-training involves compressing a large amount of text data into a neural network, while post-training involves fine-tuning the model for specific tasks.
• Thinking models are a class of LLMs that have been trained with reinforcement learning to improve their problem-solving abilities.
• Tool use is the ability of LLMs to use external tools like internet search and programming languages to augment their capabilities.
• The context window is the working memory of the LLM, where it stores the conversation history and any external information it has accessed.

Code Examples

No specific code examples are provided in the transcript, but the video mentions the use of Python code to perform calculations that are too complex for the LLM to do in its head.

Lesson Summary

In this lesson, Andrej Karpathy shares his personal experience with using Large Language Models (LLMs) for various tasks. He explains how LLMs are trained and how they can be used for practical applications like answering questions, generating text, and even doing math. Karpathy highlights the importance of choosing the right model for a specific task and demonstrates how to use tools like internet search and programming languages with LLMs. He also introduces the concept of thinking models, which are LLMs that have been trained with reinforcement learning to improve their problem-solving abilities. Throughout the lesson, Karpathy provides examples of how he uses LLMs in his own work, including using them to research topics, generate text, and even help with programming tasks.

Practice Exercise

Choose a task that you normally do manually, such as researching a topic or generating text, and try to use an LLM to do it instead. Compare the results and see how the LLM can augment your abilities.

What Is Next

In the next lesson, we will dive deeper into the capabilities and limitations of LLMs, and explore more advanced topics such as fine-tuning and customizing LLMs for specific tasks.