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Abstract:
This video serves as a practical guide to using Large Language Models (LLMs) like ChatGPT and its competitors (Gemini, Claude, Grok, Perplexity, etc.) for a general audience. It explains core concepts such as tokens, context windows, pre-training (knowledge acquisition with cutoffs), and post-training (personality/instruction following) using the analogy of an LLM as a "lossy zip file of the internet" with a specific persona. The video demonstrates various practical applications through numerous examples, including basic text generation, knowledge retrieval (with caveats about verification), and leveraging different model tiers. It introduces advanced techniques like using "thinking models" for complex reasoning, employing tools for internet search, deep research, file uploads (for analyzing PDFs/books), code execution (for calculations, data analysis, generating diagrams via Python/Artifacts), and dedicated coding assistants (like Cursor). The guide also covers multimodality, showcasing interactions via voice (distinguishing transcribed vs. native audio processing), image analysis (nutrition labels, blood tests, memes), image generation (DALL-E), video input, and AI-driven podcast creation (NotebookLM). Finally, it highlights quality-of-life features such as memory, custom instructions, and custom GPTs for personalizing interactions and streamlining repetitive tasks, particularly demonstrated through language learning examples.
Practical Guide to Using Large Language Models (LLMs)
0:00:02 Introduction & LLM Landscape: This video focuses on practical applications of LLMs beyond the basics, covering various tools like ChatGPT, Gemini, Claude, Grok, Perplexity, DeepSeek, and Mistral. It highlights the growing and diverse ecosystem.
0:02:05 LLM Leaderboards: Mentions resources like Chatbot Arena and the Scale AI leaderboard to track model performance and discover new options.
0:02:51 Basic Interaction & Tokens: Explains the fundamental text-in, text-out interaction using a haiku example. Introduces the concept of tokens (text chunks) as the underlying representation processed by the model, visualized using tools like Tiktokenizer. Explains the chat format with user/assistant turns building a token sequence.
0:07:01 Mental Model: The LLM Core: Clicking "New Chat" resets the context window (token sequence). The LLM is analogized to a "lossy zip file" of the internet (knowledge from pre-training, has a knowledge cutoff date) with an attached "smiley face" (assistant persona/style from post-training/fine-tuning). Takeaway: Initially, the LLM is a self-contained entity with potentially outdated knowledge and no external tools.
0:13:13 Basic Knowledge Queries: Demonstrates asking questions where the answer is likely common knowledge and predates the knowledge cutoff (e.g., caffeine in coffee, medication ingredients like DayQuil/NyQuil). Takeaway: Useful for general knowledge, but always verify critical information due to the probabilistic nature and potential for hallucination.
0:16:23 Best Practices: Context & Model Choice: Advises starting new chats for new topics to keep the context window relevant and efficient (avoids distraction, speeds up response). Emphasizes being mindful of which specific model and pricing tier (e.g., GPT-4o vs. GPT-4o Mini, Free vs. Plus vs. Pro) you're using, as capabilities differ significantly. Suggests using an "LLM Council" (asking multiple models) for important queries.
0:22:54 "Thinking" Models (Reinforcement Learning): Introduces models tuned with reinforcement learning (RL) that exhibit explicit reasoning steps ("inner monologue"). These are better for complex math, code, and logic problems but are slower. Examples: Debugging a coding problem (GPT-4o failed, O1 Pro succeeded), DeepSeek-R1. Takeaway: Use thinking models for hard problems needing higher accuracy, stick to standard models for speed on simpler tasks. Check UI for reasoning/thinking modes (e.g., ChatGPT 'O' models, Grok 'Think' toggle, Claude 3.7 'Extended').
0:31:01 Tool Use: Internet Search: LLMs can be given tools. Internet search is crucial for recent information (post-knowledge cutoff) or niche topics. Example: White Lotus S3 release date. How it works: Model emits a special token requesting search, the application performs the search, results (web page text) are added to the context window, model synthesizes the answer with citations. Comparison: Perplexity, ChatGPT (has search button, sometimes auto-detects need), Grok, Claude (lacked search at time of video), Gemini (dependent on specific model version). Takeaway: Use search explicitly for current/niche info; verify model/app capability.
0:39:01 Search Use Cases: Examples include checking market holidays, finding filming locations, verifying company product offerings (Vercel), getting rumors (Apple launch), finding cast lists, understanding stock movements, game releases, specific product info (toothpaste), travel safety updates, and summarizing trending news (USAID).
0:42:05 Tool Use: Deep Research: A more advanced tool combining extensive internet search over multiple sources (including papers) with thinking/synthesis (takes ~10+ mins). Example: Researching supplement ingredient Ca-AKG. Offerings: ChatGPT Pro's "Deep Research," Perplexity "Deep Research," Grok "Deep Search." Use cases: Comparing products (Brave vs Arc browser), summarizing research topics (mouse life extension), compiling market data (LLM labs - showed limitations/potential errors). Takeaway: Generates in-depth reports but treat as a first draft and scrutinize/verify citations; prone to hallucination.
0:51:01 Tool Use: File Uploads: Allows providing specific documents (PDFs, text) directly into the LLM's context window. Example: Uploading a research paper PDF (Evo 2 genomic model) for summary and Q&A. Example: Reading books (Adam Smith's Wealth of Nations) chapter by chapter, pasting text for summary and discussion. Takeaway: Augments the LLM's general knowledge with specific document context, excellent for understanding complex or unfamiliar texts.
0:59:02 Tool Use: Code Interpreter: Equips the LLM with the ability to write and execute code (typically Python). Essential for reliable calculations, data analysis, plotting. Example: Simple vs. complex multiplication (complex triggers tool use in ChatGPT). Comparison: Grok may hallucinate math, Claude uses JavaScript, Gemini was inconsistent. Takeaway: Crucial for accuracy in quantitative tasks; verify if your LLM has/uses this tool reliably.
1:04:35 Advanced Data Analysis (ChatGPT Feature): Leverages the code interpreter for data tasks. Example: Searching OpenAI's valuation history, plotting it (including log scale), fitting a trendline, and extrapolating. Caveats: The LLM might make implicit assumptions (filling missing data) or hallucinate/misstate results (incorrect extrapolation value). Takeaway: Powerful for quick data exploration and visualization, but requires careful scrutiny of the code and outputs.
1:09:05 Claude Artifacts: Claude-specific feature where the LLM generates interactive UI components (using React/HTML/CSS/JS) directly in the chat window. Example: Creating a functional flashcard app from text. Example: Generating conceptual diagrams using Mermaid syntax (visualizing Adam Smith chapter structure). Takeaway: Enables creation of simple, custom interactive tools and visualizations within the chat interface.
1:14:25 Coding Assistants (e.g., Cursor): Dedicated applications integrating LLMs deeply into the coding workflow (Cursor uses Claude/GPT APIs). Work directly with local files, providing full project context. Uses "Vibe coding" (high-level instructions) via tools like Composer. Example: Building a Tic-Tac-Toe React app, adding features like confetti and sound effects with high-level prompts. Takeaway: Far more efficient for programming tasks than web UI chatbots; enables rapid development via agent-like capabilities.
1:22:30 Multimodality: Audio Input/Output: Discusses "fake" audio (separate Speech-to-Text/Text-to-Speech models) vs. "true" audio (native processing of audio tokens by the LLM). Fake Audio: Mobile mic button, desktop tools (Super Whisper), TTS read-aloud features. True Audio (Advanced Voice Mode): Understands tone, emotion, non-speech sounds. Demos: ChatGPT advanced voice (changing voices, storytelling, counting speed, animal sounds - though sometimes refuses), Grok advanced voice (various modes like romantic, unhinged, conspiracy, sexy). NotebookLM: Generates custom podcasts from provided source documents (text, PDFs, web pages). Takeaway: Voice input adds convenience; advanced voice allows richer, more nuanced interaction; AI podcasts are useful for niche topics.
1:40:21 Multimodality: Image Input/Output: LLMs can process images represented as tokens. Image Input: Upload images (screenshots, photos) and ask questions. Examples: Analyzing nutrition labels, interpreting blood test results, understanding math equations from images, checking toothpaste ingredients, explaining memes. Best practice: Ask the LLM to transcribe text (OCR) first to verify understanding before asking complex questions. Image Output (e.g., DALL-E 3): Generate images from text prompts. Examples: Creating summary images for news, generating YouTube thumbnails.
1:49:15 Multimodality: Video Input: Demonstrated using ChatGPT's mobile advanced voice mode. Point the phone camera at objects (books, CO2 monitor, map) and ask questions in real-time. Takeaway: Enables interactive visual understanding of the user's environment.
1:52:24 Multimodality: Video Output: Brief mention of rapidly evolving AI video generation tools (Veo 2, Pika, Kling) that create video clips from text or image prompts.
ChatGPT Memory: Stores user preferences and facts learned across conversations, prepending them to future context windows. Can be managed/edited by the user.
Custom Instructions: Allows users to provide global prompts defining their identity, preferences, and desired LLM response style/tone (e.g., "don't be an HR business partner," specify Korean formality level).
Custom GPTs: Saves specific, detailed prompts (including few-shot examples) for recurring tasks, creating specialized "mini-apps." Examples shown: Korean vocabulary extractor, detailed Korean translator, Korean subtitle OCR/translator from screenshots. Takeaway: These features personalize the LLM and streamline workflows for common tasks.
2:06:45 Summary and Conclusion: The LLM ecosystem is rapidly evolving with diverse apps and features. Key aspects to consider include model capabilities (size, thinking ability), available tools (search, code, research), multimodality support (audio, image, video), quality-of-life features (memory, customization), and platform differences (web vs. mobile). Takeaway: Experiment across different platforms and features to find the best tools for your needs.