tutorials:ai_llm_tools
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| tutorials:ai_llm_tools [2025/06/11 14:32] – [What are LLMs?] chkuo | tutorials:ai_llm_tools [2025/07/24 17:23] (current) – [Use Cases] chkuo | ||
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| ==== What are LLMs? ==== | ==== What are LLMs? ==== | ||
| - | * Artificial intelligence systems trained on large collections of text to generate human-like responses | ||
| - | * Predict the next word (token) based on statistical patterns | ||
| - | * Do not “think” or “understand” like humans | ||
| - | * Mimic intelligence, | ||
| * Key concept | * Key concept | ||
| * LLMs work by estimating probability relationships among tokens, not by indexing content like a database such as Wikipedia | * LLMs work by estimating probability relationships among tokens, not by indexing content like a database such as Wikipedia | ||
| * Key terms | * Key terms | ||
| - | * Token: basic unit for LLMs to process | + | * Token: |
| * Prompt: user input | * Prompt: user input | ||
| * Session: a series of user input and LLM output | * Session: a series of user input and LLM output | ||
| * Good practice: limit the scope and length of individual sessions | * Good practice: limit the scope and length of individual sessions | ||
| - | * Memory: | + | * Memory: |
| + | * Artificial intelligence systems trained on large collections of text to generate human-like responses | ||
| + | * Human: form thoughts first, then use language to communicate those thoughts | ||
| + | * LLM: generate language by predict the next word (token) based on statistical patterns | ||
| + | * LLMs do not “think” or “understand” like humans do; the appearance of “thought” is a byproduct of those patterns | ||
| + | * LLMs mimic intelligence, | ||
| ==== Value ==== | ==== Value ==== | ||
| * Always-available conversation partners | * Always-available conversation partners | ||
| + | * Often, just having a conversation helps you think much more clearly | ||
| * Particularly helpful in the context of emotional support | * Particularly helpful in the context of emotional support | ||
| * In some ways, LLMs are like crystals of human knowledge | * In some ways, LLMs are like crystals of human knowledge | ||
| Line 75: | Line 77: | ||
| * Use LLMs as tools to sharpen your thinking, not black boxes for quick answers | * Use LLMs as tools to sharpen your thinking, not black boxes for quick answers | ||
| * Do not outsource your thinking process! | * Do not outsource your thinking process! | ||
| + | * Use LLMs in your thinking and learning: you become better | ||
| + | * Use LLMs to think for you: you skip the practice, bad for learning | ||
| * LLMs may not save time | * LLMs may not save time | ||
| * In fact, LLMs are most valuable when used to deepen the thinking process | * In fact, LLMs are most valuable when used to deepen the thinking process | ||
| Line 83: | Line 87: | ||
| * LLMs act like mirrors; their responses reflect your input and framing | * LLMs act like mirrors; their responses reflect your input and framing | ||
| * Trained to be agreeable, they often conform to both your explicit instructions and implicit tone | * Trained to be agreeable, they often conform to both your explicit instructions and implicit tone | ||
| + | * Helpful to make your implicit thoughts become explicit and better organized | ||
| * Suggested practice | * Suggested practice | ||
| * Start with a solid frame of reference | * Start with a solid frame of reference | ||
| Line 114: | Line 119: | ||
| ===== Use Cases ===== | ===== Use Cases ===== | ||
| - | * Writing and obtaining feedback as a way of thinking; force LLMs to criticize and challenge your thoughts | + | * Force LLMs to criticize and challenge your thoughts |
| * Use the "Deep Research" | * Use the "Deep Research" | ||
| - | * Reading | + | * Help to read dense papers |
| * Build custom databases of selected references using NotebookLM by Google | * Build custom databases of selected references using NotebookLM by Google | ||
| - | * Grammar | + | * Perform grammar |
| * Prepare for discussion with seminar speakers; info about you and the speaker as the input, suggestions of questions to ask as the output | * Prepare for discussion with seminar speakers; info about you and the speaker as the input, suggestions of questions to ask as the output | ||
| - | * Drafting | + | * Draft or refine |
| - | * Technology | + | * Obtain technology-related |
tutorials/ai_llm_tools.1749623529.txt.gz · Last modified: by chkuo