5/ The Eisenhower Matrix Prompt: "ChatGPT, help me organize my tasks using the Eisenhower Matrix for [Today/This Week] to maximize output."
PROMPT ENGINEERING
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Using ChatGPT to Apply the OODA Loop for Faster Decisions
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4/ The OODA Loop Prompt: "ChatGPT, how can I apply the OODA Loop to make faster, more effective decisions in [Specific Situation/Market]?"
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Prompt to Outline a Competitive Moat Using ChatGPT
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2/ Moat analysis Prompt: "ChatGPT, help me outline a moat for my [Product/Service] that will fend off competitors by focusing on [Unique Value Proposition]."
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LLMs struggle counting tokens without Chain-of-Thought or tools
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What I'm disputing is the specific, but I think common, misconception that of course any LLM could count *tokens* easily; it's just that tokenization makes letters unnecessarily hard. Without CoT or tools, LLMs are just bad at counting.
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ChatGPT passes apples-and-oranges reasoning challenge
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ChatGPT o1 passes apples and oranges reasoning challenge. Impressive. Prompt: 8 apples 5 oranges 25 bananas 8 grapes 15 strawberries 23 watermelons 1 apple 18 raspberries 5 lemons 25 kiwis 15 peaches 21 blueberries
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Ultimate Guide to Prompting Techniques and arXiv Navigation
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🆕 The Ultimate Guide to Promptinghttps://t.co/j0wMstDJ2Q
— Latent.Space (@latentspacepod) 20 septembre 2024
with @sanderschulhoff of @LearnPrompting and The Prompt Report!
Timestamps
[00:00:00] Introductions
[00:07:32] Navigating arXiv for paper evaluation
[00:12:23] Taxonomy of prompting techniques
[00:15:46] Zero-shot… pic.twitter.com/x8ZP1UomG7The Ultimate Guide to Prompting https://
latent.space/p/learn-prompt
ing
… with @sanderschulhoff of @LearnPrompting and The Prompt Report! Timestamps
[00:00:00] Introductions
[00:07:32] Navigating arXiv for paper evaluation
[00:12:23] Taxonomy of prompting techniques
[00:15:46] Zero-shot -
Anthropic’s prompt caching enables more efficient LLM algorithms
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Anthropic's prompt caching really should be better known. A lot of features that distinguish LLM vendors are incremental nice-to-haves, but prompt caching enables algorithms otherwise too slow and costly to consider:
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Stanford paper reveals Chain of Thought unlocks sequential tasks.
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Stanford paper might be the key to OpenAI o1’s performance: What’s so effective about Chain of Thought? ⇒ it unlocks radically different sequential tasks! Reminder: A Chain of Thought (CoT) means that you instruct the model to “think step by step”. Often it’s literally
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Prompt for underwater photorealistic image generation
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Expert (créations avec Prompt). Créez une image photoréaliste d'une femme sous l'eau, avec ses cheveux flottant autour d'elle dans l'eau. Le décor doit être serein et éthéré, capturant le jeu de la lumière et de l'eau. L'expression de la femme doit être calme et captivante,
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Clarifying tokenization’s role in LLM inference
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I fully concede tokenization causes important problems and an LLM trained without them would be more interesting than this thread. I’m only disproving a specific (but common I think) misunderstanding of its role in inference that does predict against these observations, namely: