Yeah, it seemed like there was more than necessary, but I haven’t played around with metaprompting much so got some nice ideas.
PROMPT ENGINEERING
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Prompt Rewriting and Ontology-Based Example Generation for LLMs
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Pretty interesting. I like the prompt rewriting approach they have. I think I also saw they use the ontology to have the LLM generate examples (for context) that match the user set ontology, which is nice and makes sense.
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Names Are All You Need: Model Sensitivity to Naming
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Names are all you need. We noticed the same – the models are very sensitive to naming (function names, fields,…) even more so than descriptions.
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Photorealism Effectiveness for SDXL Photograph-Style Images
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Does "photorealism" actually work to get photograph-style images from SDXL? Check out this case study made using Replicate!
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Creativity: Essential Soft Skill for Effective GenAI Interaction
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Why is #creativity is an essential #softskill for effectively interacting with AI, especially #genAI? Listen to this episode of #AIToday to learn more
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Comprehensive AI and Prompt Engineering Bundle for Business
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The Complete AI Bundle for Your Business Success in 2024: Learn more: https://
godofprompt.ai/complete-ai-bu
ndle
… What’s inside Mega-Prompts for Marketing Mega-Prompts for Business Custom GPTs Toolkit Ultimate ChatGPT Prompt Library Ultimate Midjourney Prompt Library -
Effectiveness of Plain Prompting vs Fine-Tuned Models for AI Agents
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It's trendy to share models "fine-tuned for function calling", e.g. Command-R-Plus or Mixtral-8x22B. But you don't need this to make good agents Cf graph:
The count of incorrectly formatted actions is already close to 0 with plain prompting! (GPT-4o, GAIA validation run) -
Reducing Concept Strength to Prevent Overfitting
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Lowering concept strength to something like 0.2 when generating tends to reduce overfitting
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What Prompt Creates This Amazing AI-Generated Content?
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Amazing! I wonder what the prompt would be to create this.
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Gen AI translates English requirements into executable programs
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Gen AI bring us one step closer to this world — where we can express requirements in English, and use models to translate them to programs. When used as part of a program, they allow us to work with unstructured data, and provide a way to deal with uncertainty.