Great post! We've got some additional resources for folks looking to better understand Retrieval Augmented Generation (RAG) and even see it in action - in this

Great post! We've got some additional resources for folks looking to better understand Retrieval Augmented Generation (RAG) and even see it in action - in this example we demonstrate a potentially very dangerous hallucination (that has to do with driving) and how to fix it using RAG: https://www.pinecone.io/learn/retrieval-augmented-generation/ https://www.pinecone.io/learn/retrieval-augmented-generation... If you're curious to actually try out the difference between an LLM without domain-specific context and an LLM that is using RAG, you can try our live demo here: https://pinecone-vercel-starter.vercel.app/ https://pinecone-vercel-starter.vercel.app/ And if you'd like to fork and make your own tweaks to the above demo ^ chatbot, in order to, for example, swap in your own company logo and extend it for your purposes, you can find our Vercel template here: https://github.com/pinecone-io/pinecone-vercel-starter https://github.com/pinecone-io/pinecone-vercel-starter In our opinion, RAG is indeed an effective technique partly because you don't need to be a machine learning expert in order to implement it in your Generative AI applications.