Creating an AI Chatbot to interact with 6000+ tools using Zapier and LangChain This great walkthrough by @matchaman11 on how he build EmbedAI to do exactly that Read all about it below! https://
medium.com/@anilmatcha/cu
stom-gpt-with-ai-actions-chatbot-tutorial-creating-an-ai-chatbot-to-interact-with-6000-tools-b0d78211c15a
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PROMPT ENGINEERING
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AI Chatbot Zapier LangChain Integration Guide EmbedAI
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Language Models for Decision-Making: Prompt Engineering and Human Evaluation
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To do so, we created a wide range of prompts emulating how people could use a language model when making a decision about another person.
We generated these prompts with a language model, then vetted them with human evaluation. -
AI Tutor with RAG: Precise Answers for Coders and Students
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The AI Tutor, empowered by Retrieval Augmented Generation (RAG), provides precise answers from resources like technical articles and Wikipedia, improving with each query for coders and AI students tired of sifting through forums and old docs.
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iPrompt and Tree Prompting: Advanced Techniques for AI Model Enhancement
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yes! 🙂 iPrompt: http://
arxiv.org/abs/2210.01848
Tree Prompting: http://
arxiv.org/abs/2310.14034 (thanks for asking) -
Negative Embeddings for Improved Pimple Detection Models
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Have you tried a negative embedding tuned on pimples etc?
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Chain-of-thought: a necessary improvement for LLMs
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Chain-of-thought prompting gives superior responses in LLMs. OpenAI should have implemented that a while ago. That's not "cheating" – that's programming.
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iPrompt and Tree Prompting Techniques Presented at Blackbox NLP
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iPrompt and Tree Prompting were joint work with the prolific @csinva
. come see us today at the blackbox NLP workshop!! -
Research on Language Model Interpretability and Prompt Engineering at EMNLP2023
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i am in singapore, presenting some cool research at #EMNLP2023!! • iPrompt: Explaining Patterns in Data with Language Models via Interpretable Autoprompting
• Text Embeddings Reveal (Almost) As Much as Text
• Tree Prompting: Efficient Task Adaptation without Fine-Tuning -
Mastering Claude 2.1’s 200K Token Context Window Effectively
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Claude 2.1’s 200K token context window is powerful, but requires careful prompting to use effectively. Learn how to get Claude to recall an individual sentence across long documents with high fidelity: https://
anthropic.com/index/claude-2
-1-prompting
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