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How I use LLMs

By Andrej Karpathy

🤖 AI Summary - Exploring Large Language Models (LLMs) and AI Tools

Introduction to Large Language Models (LLMs)

  • The video continues a general audience series on Large Language Models (LLMs), particularly ChatGPT.
  • Previous videos covered how LLMs work under the hood; this one focuses on practical applications.

Evolution of LLMs

  • ChatGPT’s Launch (2022): First major LLM deployment by OpenAI, allowing users to interact with AI through a text-based interface.
  • 2025 AI Ecosystem: The space has evolved, with competitors like:

Where to Track AI Model Performance

How LLMs Work

  • Inputs and outputs are broken into tokens.
  • LLMs predict the next token based on patterns learned during training.
  • Limitations & Knowledge Cutoff – LLMs rely on pre-existing data and may lack real-time updates.
  • AI can visit multiple web pages, extract relevant information, and summarize results.
  • Search Token Mechanism: Some AI models use a search token to trigger live web searches.
  • Comparison of AI Search Capabilities – Different AI models handle search differently (e.g., ChatGPT vs. Claude).

Deep Research Feature in AI

  • A new feature allowing AI to spend more time on complex research.
  • Combines internet search and reasoning to generate detailed reports.

LLMs and File Processing

  • Uploading Documents for Analysis – AI can analyze PDFs, scientific papers, and books.
  • Interactive Learning: AI can provide summaries, historical context, and explanations.

AI for Data Analysis and Code Generation

  • LLMs and Coding Assistance – AI can generate code but must be verified.
  • AI for Advanced Data Analysis (ADA) – Creating graphs, trend predictions, and statistical modeling.
  • Claude’s “Artifacts” Feature – Interactive coding with real-time JavaScript and React execution.

AI-Assisted Coding in VS Code & Cursor

  • Cursor as an AI Coding Assistant – AI-driven project modifications in VS Code.
  • “Vibe Coding” Approach – AI automates repetitive coding tasks.

AI and Multimodal Capabilities

  • LLMs now process images, audio, and video.
  • Example: Analyzing a nutrition label from a supplement mix and categorizing ingredients.

AI for Medical and Scientific Analysis

  • Blood Test Interpretation: AI can transcribe blood test results, provide insights, and highlight concerns.
  • AI in Mathematics and Science: Recognizing and solving complex equations from an image.

AI for Everyday Object Analysis

  • Example: Uploading a toothpaste ingredient list to analyze which chemicals are essential vs. unnecessary.

AI Image and Video Generation

  • DALL·E 3 – Generates high-quality images based on text prompts.
  • AI-generated videos – Tools like Runway, Sora, and Pika compete in video generation.

Custom GPTs and AI Assistants

  • Users can build custom GPTs to automate tasks.
  • Examples:
    • Korean Vocabulary Extractor – Turns sentences into flashcards.
    • AI Subtitle Reader – Extracts and translates subtitles from screenshots.

Timestamps for Key Sections

Introduction to Large Language Models (LLMs)

AI-Powered Search and Research

AI for Data Science & Coding

AI for Multimodal Understanding

AI-Generated Content and Personalization

Graph View

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