From full creative freedom to strict fact matching, organizations can design chatbots that meet their specific risk tolerance and communication needs. The key is choosing an approach that serves the user while protecting the brand.
The matchup we have all been waiting for! A fine-tuned upstart underdog against the reigning champion heavyweight LLM! Will our tiny model prevail?
While my model is training, let’s take a moment to pause and reflect on the process so far, its thorns and roses, and make a few more Bert puns while we’re at it!
You can’t have RAG without a retriever, but what exactly is a retriever? And what kind of retriever am I going to use? This post will take a trip with BERT, and talk about some recent innovations with the model. Let’s jump in! Parts of a retriever All retrievers have at least three things in common: 1. A corpus of information is stored somewhere, somehow 2. When presented with an incoming query, they do some stuff to process it 3.
OK, here’s my question: Why do all these RAG apps use giant f**king models?? Default Langchain RAG tutorial - ChatGPT 3.5 Turbo (175B param) Random RAG tutorial from Google - ChatGPT 3.5 Turbo (175B param) Another tutorial - Llama 2 (70B param) There are more, but you get the point. I’m worked up about this because these foundation models- chatGPT, Claude, Bard, the whole lot- are so freaking powerful that using them for information retrieval is super inefficient.