YACCS = Yet Another #ChatGPT Cheat Sheet (actually, more than one)
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And this one also (30-page PDF download): https://
dropbox.com/s/jolrxxqgfac5
krv/yetanotherChatGPTCheatSheet.pdf?dl=0
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#AI #GenerativeAI #DeepLearning #GPT3 #GPT4 #NLproc #MachineLearning
PROMPT ENGINEERING
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YACCS: Yet Another ChatGPT Cheat Sheet Collection
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Claude Document Analysis: Business Information Retrieval
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Claude can help retrieve information from business documents. Drop multiple documents or even a book into the prompt and ask Claude questions that require synthesis of knowledge across many parts of the text.
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Keyword Search Limitations in AI-Powered Systems
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2/5 Keyword search? It was all about finding responses with the most common words. But it's not foolproof. For example, ask "Where is the world cup?" and you might get "Where in the world is my cup of coffee?" Not helpful, right?
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Midjourney guides and online resources for AI generation
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http://
Midjourney.com There are so many guides online. -
Building Super-Powerful AI Agents Together
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Please play with it, add your tools, and let's create *super-powerful agents* together. Here's a notebook to get started:
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How AI Agents Work: Prompt Building and Chain-of-Thought
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How does it work in practice? It's straightforward prompt-building:
• Tell the agent what it aims to do
• Give it tools
• Show examples
• Give it a task The agent uses chain-of-thought reasoning to identify its task and outputs Python code using the tools. -
Extensible AI Tool with Multimodal Built-in Capabilities
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It comes with built-in tools: • Document QA
• Speech-to-text and Text-to-speech
• Text {classification, summarization, translation, download, QA}
• Image {generation, transforms, captioning, segmentation, upscaling, QA}
• Text to video It is EXTENSIBLE by design. -
Two-step planning improves language model task performance
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To fix this, we're separating the planning and execution into two steps: – plan what to do
– execute on each step This usually leads to more calls to the language model… but helps it stay on track for more complex tasks -
LangChain Introduces Plan-and-Execute Agents for Complex Tasks
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Plan-and-Execute Agents Inspired by BabyAGI and the recent Plan-and-Solve paper, we're introducing a new type of @langchain agent We think these are better for more complex tasks, at the cost of more calls to the LLM Blog: https://
blog.langchain.dev/plan-and-execu
te-agents/
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