With images and text, it seems perfectly reasonable to generate infinite variants till you’re happy. Code and video require too much manipulation of generations if you want changes and that requires skill.
PROMPT ENGINEERING
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ImageAI Differs from CodeAI and VideoAI in Usage Requirements
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ImageAI is different in the way it is used versus codeAI or videoAI. If you want to modify anything that code AI generates, you need to understand code. Same w video. The output generated will never be perfect and you will want to edit some files (code) or frames (video). More
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Understanding Concept Representations in Text-to-Image Diffusion Models
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The Hidden Language of Diffusion Models
— AK (@_akhaliq) 2 juin 2023
paper page: https://t.co/biwX1KX7hG
tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a… pic.twitter.com/mF0QzNxAjoThe Hidden Language of Diffusion Models paper page: https://
huggingface.co/papers/2306.00
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… tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a -
Karpathy’s GPT State Talk: Training and Prompting Strategies
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Excellent talk by @karpathy on the State of GPT, breaking down everything from the training pipeline to the most effective prompting strategies for LLMs. https://
youtube.com/watch?v=bZQun8
Y4L2A
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The Evolution of Prompt Engineering Beyond Simple Model Nudging
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People always ask if prompt engineering is going to go away over time. My short answer is "no". But, a more nuanced answer is that the goal of prompt engineering has evolved over time: from nudging a finnicky language model to do an "easy" task (2020/2021) to figuring out how to
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5 ChatGPT prompts to boost intelligence and creativity
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These 5 #ChatGPT prompts will boost your brain → https://youtu.be/ZofS7u4VH2E With them, you will be more intelligent and more creative!
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Improving CLIP Training with Language Rewrites via LaCLIP
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Improving CLIP Training with Language Rewrites introduce Language augmented CLIP (LaCLIP), a simple yet highly effective approach to enhance CLIP training through language rewrites. Leveraging the in-context learning capability of large language models, we rewrite the text
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LLMs Prompts Output Parsers and Evaluation Fundamentals
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we covered a lot but i liked the prompts/llms/output parsers section (basic building blocks, important to understand) and the evaluation section (very practical regardless of use case)
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LangChain for Building LLM Applications: Memory, QA, Agents
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3/LangChain for LLM Application Development: Learn this powerful open-source tool for building applications using LLMs, including memory for chatbots, Question answering over a doc, and an LLM agent that can decide what action to take next. https://
learn.deeplearning.ai/langchain -
Building Complex Systems with ChatGPT API
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2/Building Systems with the ChatGPT API: Go beyond individual prompts, and learn to build complex applications that use multiple API calls to an LLM. Also learn to evaluate an LLM's outputs for safety and accuracy, and drive iterative improvements. https://
learn.deeplearning.ai/chatgpt-buildi
ng-system
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