What’s does RAG announcements from OpenAI Dev Day really mean?

It’s no surprise that OpenAI DevDay got the same attention that Apple used to get in Steve Jobs’s days. Does the new Reteriver-Augemented Generation tools using OpenAI hold up?

Key Highlights from what was announced and what it really means for AI engineering folks worldwide.

• The top highlight was the creation of a domain-specific agent using a single command in a notebook. A lot of talks about RAG (Retriever Augmented Generation) in this context, since that’s the only way enterprises can harness the power of GenAI with their own private knowledge base. You can create an agent to answer any question on your website for a task like “sales or billing” and supply it with relevant documents and it will be ready. The whole “AI agent creation” has been talked around for a while but this has taken simplification to the next level. I liked that it’s not GUI-based but rather command-based. Take a look at the below images.

• - The new context window 128K is a game changer. This solution is technically not RAG as it does not truly “Retrieve” anything from a pile of documents by searching it, ranking it & and then passing the best one to LLM. It is really magic of big context length at work; which works on the pile of documents (I think the max is like equivalent of 300 pages).
• - This does put some “simpler” RAG use cases like askpdf, ask my documents, summarise it, and contract analysis at a click away.

• It does not really solve the “R” (from RAG) problem for any enterprise that has more than a few hundred pages in its knowledge base (I guess all companies) and wants to find the best answers for a question in a fluent manner. The accuracy of the retriever, re-ranking them, and measuring the veracity of generated answers remains a challenge in such cases. However, OpenAI has come up with a recipe for it, which is not very different from what is already applied by open-source.
• - The questions remain around data sovereignty. The RAG pipeline built over open source offers multi-tenancy and does not transfer data out of their env. It remains to be seen how those will be solved in the coming days.

• - Also the Python SDK is finally out. This will have long term impact on the GenAI application ecosystem. (https://lnkd.in/ewJnnNPe)

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