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