THE FUTURE IS HERE

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

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/generation/generative-qa/openai/gen-qa-openai/gen-qa-openai.ipynb

🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/nlp-transformers

🎨 AI Art:
https://www.etsy.com/uk/shop/IntelligentArtEU

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👾 Discord:
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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