Lecture Notes on Large Language Models and Practical Applications

Introduction to Large Language Models (LLMs)

• Previous video covered the fundamentals of LLMs.
• Current focus: practical applications of LLMs.
• LLMs allow interaction via text interface.

Ecosystem of LLMs

• ChatGPT: Developed by OpenAI, deployed in 2022.
  • Went viral as the first widely accessible LLM.
• Growth of the Ecosystem: Many apps similar to ChatGPT have emerged by 2025.
  • Big Tech Examples: Gemini (Google), Meta, Copilot (Microsoft).
  • Startups: Anthropic’s Claude, xAI’s Grok.
• International Presence: Companies like DeepSeek (China) and Mistral (France).

Tracking and Comparing LLMs

• Tools for Tracking: Chatbot Arena, Seal LLeaderboards.
• Provides rankings and performance evaluations of different LLMs.

Interacting with ChatGPT

• Text input leads to text output (e.g., writing haikus).
• Internally, LLMs translate text into a sequence of tokens.
• Tools like TikTokenizer can show how text is tokenized.

Conversation Format

• User queries and model responses form a token sequence.
• Each conversation stored as a sequence of tokens within a context window.
• New conversations reset the token context.

Structure of LLMs

• LLMs trained through pre-training (knowledge acquisition) and post-training (personality/response style).
• Pre-training involves compressing the internet into a probabilistic model.
• Post-training adjusts the model to respond like a friendly assistant.

Practical Questions and Usage

• Example questions: caffeine content in drinks, medication queries.
• Importance of verifying LLM responses, as they might not always be accurate.

Efficient Usage Tips

• Begin new chats when switching topics to avoid context clutter.
• Keep track of which model/version you’re using (e.g., GPT-4, Claude).
• Different pricing plans affect model access and capabilities.

Thinking Models

• Models are trained with reinforcement learning to develop reasoning capabilities.
• Useful for solving complex problems like math and coding.

Tool Use

• Internet Search: Allows LLMs to pull recent data from the web.
• Deep Research: Combines search and reasoning over extended periods.
• Python Interpreter: Executes code to solve problems beyond the LLM’s native capabilities.

Multimodal Capabilities

• Speech and Audio: LLMs can transcribe and synthesize speech.
• Images and Video: LLMs can process and generate visual content.

Applications and Features

• Advanced Data Analysis: For plotting and data visualization.
• Cursor: A coding assistant that works with VS Code.
• Voice Modes: Advanced voice interaction in mobile apps.
• Memory Feature in ChatGPT: Enables storing user preferences and info.
• Custom GPTs: Create personalized LLMs for specific tasks.

Conclusion

• The LLM ecosystem is rapidly expanding with various applications and tools.
• Users should explore different LLMs and tools to maximize efficiency and effectiveness in their tasks.