When you want a language model to do productive work, providing it with the right context and grounding it with organizational data is key to preventing unwanted outputs. But building on foundational models can be complex and time-consuming – especially if you consider fine-tuning them on your own data. So, most often, Retrieval-Augmented Generation (RAG) is implemented to provide the necessary context, but building RAG pipelines– vector DBs, embeddings, indexing – can be a significant undertaking. How do we make this simpler and more approachable for developers starting to build AI applications? With KAITO, a CNCF sandbox project, you can start to tame the complexities and quickly build RAG pipelines using the RAGEngine CRD– reducing the need for complex coding. KAITO streamlines this process, allowing developers to focus on application logic rather than infrastructure management. Join us to learn how KAITO can simplify your RAG pipelines and accelerate your AI application development