100% – I spent a year trying to prompt a good writing style and am still not there yet – 40% of my texts I rather have to dictate than delegate to AI.
PROMPT ENGINEERING
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NotebookLM Deep Dive Podcast with Chase and Yao
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Here is an 18min podcast about our podcast with @hwchase17 and @ShunyuYao12 from NotebookLM Deep Dive.
— Latent.Space (@latentspacepod) 30 septembre 2024
let us know if you like this better than the… yknow… actual podcast
because then we dont have a job anymore ig https://t.co/iOnSfTrMcC pic.twitter.com/AEpCQpwgAkHere is an 18min podcast about our podcast with @hwchase17 and @ShunyuYao12 from NotebookLM Deep Dive. let us know if you like this better than the… yknow… actual podcast because then we dont have a job anymore ig
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Selecting and Adapting Recipes for Model Implementation
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The safest way is to pick a recipe that already works and transpose it to your use case. That requires using the same source model indeed.
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Making TEN Agent Speak Meow Meow
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Let's make TEN Agent speak meow meow. pic.twitter.com/IEMeHwPLoj
— 艾略特 (@elliotchen100) 30 septembre 2024Let's make TEN Agent speak meow meow.
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Using ChatGPT as an AI Sales Assistant
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I turned ChatGPT into my sales assistant. Now, I never worry about finding new customers. These are the 11 powerful prompts I used ↓
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Clean UI for Llama-3.2-11B-Vision Image Description Tool
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Clean-UI for Llama-3.2-11B-Vision This project provides a simple and clean UI for interacting with the Llama-3.2-11B-Vision model. You can upload images and input prompts to generate conditional image descriptions with this UI. The interface is built using Gradio and
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Minstrel: Multi-Agent System with Reflection for Automated Prompt Engineering
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10). Minstrel – a multi-generative agent system with reflection capabilities to automate structural prompt generation; it presents LangGPT, an extensible framework for designing prompts.
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RAG Task Categorization Survey: External Data Classification
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7). RAG and Beyond – presents a survey that introduces a RAG task categorization method that helps to classify user queries into four levels according to the type of external data required and the focus of the task.
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Logic-of-Thought: Propositional Logic Prompting Technique
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6). Logic-of-Thought – proposes a new prompting technique called Logic-of-Thought (LoT) which employs propositional logic to generate and inject expanded logical information from the input context.
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Cognitive Architecture and Prompting Strategies with LangGraph
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Less about the framework, more about the prompting/cognitive architecture. I think there are a few examples of doing it with Langgraph,