Building a Retrieval-Augmented Generation (RAG) pipeline can seem like a puzzle. There are a lot of pieces to consider, like the size of your data set, what kind of content you're working with, your budget, and the level of performance and precision you're aiming for. To make things even more challenging, these pieces often affect each other.

That's why, when developers ask us, "how should we build our RAG pipeline?", we usually say, "it depends." There's no one-size-fits-all answer because each situation is unique.

But don't worry, we're here to help you put this puzzle together. We've created an interactive questionnaire that can guide you in the right direction. Based on answers to questions about your situation, it will give you recommendations on several key choices you'll need to make. The purpose of this tool is not to replace a formal evaluation, but to give you some idea of what some good first steps could be.

For example, it will suggest the best way to store your raw data, keeping in mind factors like speed and efficiency. It will also recommend an embedding model that's a good fit for your data and goals, helping to improve the accuracy of your results.

On top of this, the questionnaire will help you choose a chunking strategy for processing your data more efficiently. And lastly, it will suggest a data processing method that suits your pipeline, considering the type of data you're working with and what you want to achieve.

Ready to get started? Access the interactive questionnaire and begin designing your own RAG pipeline now.