Generative AI and Long-Term Memory for LLMs (OpenAI, Cohere, OS, Pinecone)

ames Briggs
Generative AI and Long-Term Memory for LLMs (OpenAI, Cohere, OS, Pinecone)
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Generative AI is what many expect to be the next big technology boom, and being what it is — AI — could have far-reaching implications far beyond what we'd expect.

One of the most thought-provoking use cases of generative AI belongs to Generative Question-Answering (GQA). Now, the most straightforward GQA system requires nothing more than a user text query and a large language model (LLM). We can test this out with OpenAI's GPT-3, Cohere, or open-source Hugging Face models. However, sometimes LLMs need help. For this, we can use retrieval augmentation. When applied to LLMs can be thought of as a form of "long-term memory" for LLMs. 🌲 Pinecone article: https://www.pinecone.io/learn/openai-... 📌 Notebook: https://github.com/pinecone-io/exampl... 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/nlp-transformers 🎨 AI Art: https://www.etsy.com/uk/shop/Intellig... 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/member... 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 What is generative AI 01:40 Generative question answering 04:06 Two options for helping LLMs 05:33 Long-term memory in LLMs 07:01 OP stack for retrieval augmented GQA 08:48 Testing a few examples 12:56 Final thoughts on Generative AI

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