RAG (Retrieval-Augmented Generation) isn’t something that gets “added” to an LLM, it’s an architectural pattern. It retrieves relevant data (from a vector store, database, etc.) and feeds it into the LLM as context. The model then generates responses using that extra information, but the retrieval system lives outside the model itself.
You’re taking a really different approach from how I built my RAG, and I love it!
The way you’re handling hallucination is super considerate. Definitely something I want to try in my own setup.
Thank you, Jenny!
Do you think OpenAI will bake this into version 5, it seems like a no brainer?
Claudia, the timing couldn't be more perfect! I just texted a friend:
Btw — if you come across some “understanding RAG” without being technical content/projects , pls pass along
Haha nice! Hope this clarified things for you. Feel free to ask if you have follow-up questions after reading this
RAG (Retrieval-Augmented Generation) isn’t something that gets “added” to an LLM, it’s an architectural pattern. It retrieves relevant data (from a vector store, database, etc.) and feeds it into the LLM as context. The model then generates responses using that extra information, but the retrieval system lives outside the model itself.