This is How Andrej Karpathy Uses LLMs

Andrej Karpathy's latest video, "How I Use LLMs," provides a comprehensive overview of the rapidly evolving Large Language Model (LLM) ecosystem. Building on his previous "Deep Diving into LLMs" video, Karpathy showcases how LLMs have transitioned from simple text-based chat interfaces to sophisticated, multi-modal platforms integrating diverse tools and functionalities. This article summarizes his key insights and demonstrations.

Table of Contents

• The Expanding LLM Landscape
• Beyond Text: Multi-Modal Capabilities
• "Thinking" Models: Deliberate Problem Solving
• Tool Integration: Web Search and In-Depth Research
• Advanced Research: Synthesizing Information from Multiple Sources
• Document and Multimedia Processing via File Uploads
• Python Interpreter: Code Execution and Data Analysis
• Custom Visual and Code Tools: Claude Artifacts and Cursor Composer
• Audio Interactions and Podcast Generation with NotebookLM
• Visual Processing: Image Input/OCR, Image Generation, and Video
• Personalization: Memory, Custom Instructions, and Custom GPTs
• Tips for LLM Beginners
• Conclusion

The Expanding LLM Landscape

Karpathy highlights the growth beyond the pioneering ChatGPT, mentioning competitors like Gemini, Copilot, Claude, Grok, DeepSeek, and LeChat, each offering unique strengths and pricing models. He suggests using resources like Chatbot Arena and Scale's Leaderboard to compare model performance.

Beyond Text: Multi-Modal Capabilities

Karpathy delves into the multi-modal capabilities of LLMs, moving beyond text generation.

Text Generation: LLMs excel at creative writing tasks (poems, emails, etc.), with interactions visualized as dynamic "chat bubbles." He explains the underlying mechanics of tokenization, context windows, and the role of POS tagging and NER. Different tokenization algorithms (like Byte-Pair Encoding) and special tokens (: and ) are discussed.

The two-stage training process (pre-training and post-training) is detailed, emphasizing the cost and limitations of pre-training and the importance of post-training for human interaction and reducing hallucinations. He also discusses decoding and sampling techniques (nucleus sampling, top-k sampling, beam search).

Image and Video: Karpathy demonstrates image generation by combining captioning and image-generation models. He also shows video capabilities, where the LLM "sees" via a camera feed and identifies objects.

Audio: He highlights voice interaction, differentiating between "fake audio" (text-to-speech) and "true audio" (native audio tokenization). The ability to generate audio responses in various personas is showcased.

"Thinking" Models: Deliberate Problem Solving

Karpathy explores "thinking models," which utilize reinforcement learning to reason through complex problems step-by-step. He contrasts these with standard models, illustrating how thinking models can provide more accurate solutions, albeit at the cost of increased processing time. He uses a gradient check failure example to highlight the difference.

Tool Integration: Web Search and In-Depth Research

The integration of internet search capabilities is discussed, showing how LLMs can access and process real-time information, overcoming knowledge cutoffs. He compares the search integration of different models (Claude, Gemini, ChatGPT, Perplexity.ai).

Advanced Research: Deep research, often requiring higher-tier subscriptions, is explained as a process combining extensive web searches with reasoning to create comprehensive reports, complete with citations.

File Uploads, Python Interpreter, Custom Tools, and Personalization

The article then covers file uploads for processing documents and multimedia, the integrated Python interpreter for code execution and data analysis, custom visual and code tools (Claude Artifacts and Cursor Composer), and the importance of personalization features like memory, custom instructions, and custom GPTs. Examples of each are provided.

Tips for LLM Beginners and Conclusion

The article concludes with advice for beginners and a summary of Karpathy's key takeaways, emphasizing the blend of mathematical principles and data compression that underlies the power of LLMs. The rapidly evolving nature of the field is highlighted, encouraging continuous learning and experimentation.

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