In the race to build better AI applications, there’s often an assumption that bigger is better when it comes to language models. However, with careful attention to retrieval-augmented generation (RAG) pipeline design and prompt engineering, smaller, more cost-effective models can often perform just as well as their larger counterparts. Here’s how you can optimize your […]
If you’re implementing retrieval augmented generation (RAG), there’s one crucial component you might be missing: a reranking model. While vector similarity search has become the go-to method for retrieving relevant context, relying solely on similarity scores can lead to suboptimal results. Let me show you why reranking is not just an optional enhancement, but a […]
At Vectorize, we want to make it fast and easy for our users to create retrieval-augmented generation (RAG) pipelines to power their AI applications. We’ve tried to make it as intuitive as possible and continue to iterate on our user interface towards this goal. However, when you support dozens of integrations with third-party vector databases, […]
Data pipelines move data from sources to destinations for analysis. It can serve as the pathway for data traveling from its original sources to its destination for analysis. It’s a process that will take the data from numerous sources so they can not only be analyzed, but also visualized as well. We will zoom in […]
