I gotta say – I love it living in exponential times. I can just wish that something existed and then within a month it does! This time it happened with OpenAI's 4o image generation release. In this blog post I'll briefly cover the release and why I think it's pretty cool. Then I'll dive into a new opportunity that I think is even more exciting – visual reasoning.
I've always been interested in entrepreneurship, so, early on in my career, I asked my financial advisor for book recommendations about startups. He handed me "The E-Myth" by Michael Gerber – a book about... building food service franchises? In the heat of the dot-com explosion, this wasn't exactly the startup guide I was hoping for, but its core message stuck with me and turned out to be surprisingly relevant to the problems I hear about regularly when talking to people about building reliable LLM applications.
Let's face it, RAG can be a big pain to set up, and even more of a pain to get right.
There's a lot of moving parts. First you have to set up retrieval infrastructure. This typically means setting up a vector database, and building a pipeline to ingest the documents, chunk them, convert them to vectors, and index them. In the LLM application, you have to pull in the appropriate snippets from documentation and present them in the prompt so that they make sense to the model. And things can go wrong. If the assistant isn't providing sensible answers, you've got to figure out if it's the fault of the prompt, the chunking, or the embedding model.
If your RAG application is serving documentation, then there might be an easy alternative. Rather than setting up a traditional RAG pipeline, put the LLM assistant to work. Let it navigate through the documentation and find the answers. I call this "Roaming" RAG, and in this post I'll show you how it's done.
Most chat applications are leaving something important on the table when it comes to user experience. Users are not satisfied with just chit-chatting with an AI assistant. Users want to work on something with the help of the assistant. This is where the prevailing conversational experience falls short.
In information retrieval, we often find ourselves between two tools: keyword search and semantic search. Each has strengths and limitations. What if we could combine the best of both?
By the end of this post, you will:
If you've struggled with inaccurate search results or wanted a more transparent search system, this post is for you. Let's explore how SPLADE can change your approach to information retrieval.