Prompt builder @fkadev uploaded a list of 137 creative prompts to http://
huggingface.co that demonstrate the creative power of ChatGPT. This list is also a great way to introduce someone to ChatGPT who hasn't used it before (like your parents) Here's what a few look like:
LLMS
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Curated list of 137 creative prompts for ChatGPT
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The Art of Prompt Engineering for AI Conversations
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Prompts act as a starting point for a conversation with ChatGPT, and prime it for an ongoing conversation to assist the user in answering questions in a specific way to achieve the desired result. Like everything else, there's an "art" to it.
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AI Chatbot Generates Python Code for UMAP Visualization of Handwritten Digits
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Using Poe Sage (GPT-3.5 chatbot from @Quora
) to ask a fast-talkin' wise guy from a 1940s gangster movie how to use UMAP to visualize the topological manifold of UCI ML handwritten digits. "Snazzy" Python code and Matplotlib plot shown in reply. https://
poe.quora.com/s/kjbVTU3hH7mL
b82H1L6S
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Discussion on GPT-3 Tokenization and Spacing
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Just search “GPT-3 tokenization”. It hasn’t changed since GPT-2 days, I don’t think. I just used dashes because you need spaces to be spaces between words.
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LLM Tokenization: Not Vectors or Letters, but Token Sequences
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It doesn’t see words as embedded vectors or as letters, it sees them as token sequences — chunks of about 4 letters from a fixed mapping of strings to tokens. It does better on letter-based tasks if you ask it to first rewrite words l-i-k-e t-h-i-s.
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Critique of AI Model Hallucinations: GPT-3 vs. ChatGPT
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That isn't ChatGPT. GPT-3 hallucinates in many scenarios.
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LangChain Use Cases: Agents, Chatbots, QA, Summarization
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Use Cases A new section, this highlights common use cases of LangChain, including: – Agents
– Chatbots
– Question Answering over your documents
– Summarization
– Evaluation https://
langchain.readthedocs.io/en/latest/inde
x.html#use-cases
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LangChain Ecosystem Integration with Major AI Players
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Ecosystem A new section as well, this highlights how LangChain integrates with other players in this space, including: @OpenAI @CohereAI @huggingface @weaviate_io @pinecone @AI21Labs @HazyResearch https://
langchain.readthedocs.io/en/latest/ecos
ystem.html
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Getting Started with LangChain for LLM Application Development
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Getting Started Covers all aspects of how to get started using LangChain to build an LLM application – including installation, environment setup, and a walkthrough of the main modules https://
langchain.readthedocs.io/en/latest/gett
ing_started/getting_started.html
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LangChain Core Modules: Prompts, LLMs, Chains, Agents
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Modules The main modules of functionality that make up LangChain, consisting of: – Prompt Templates
– LLMs
– Utilities (search, vector DBs, etc)
– Chains
– Agents
– Memory