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

Generative AI and LLMs

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-gen-qa/ 📌 Notebook: https://github.com/pinecone-io/examples/blob/master/learn/generation/openai/gen-qa-openai.ipynb 🤖 AI Dev Studio: https://aurelio.ai 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 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

About the course

The Generative AI and Large Language Models (LLMs) course covers everything you need to know about: - Generative AI - Large Language Models (LLMs) - OpenAI, Cohere, Hugging Face - Managed vs. Open Source - LLM Libraries like LangChain and GPT Index - Long-term memory and retrieval-augmentation And more to come...