Challenges of Building a RAG Pipeline and How Backboard Solves Them
The article discusses the difficulties of building a Retrieval-Augmented Generation (RAG) pipeline from scratch and how Backboard simplifies this process by handling the underlying infrastructure.
Why it matters
The article highlights the challenges of building a RAG pipeline from scratch and how Backboard's turnkey solution can save developers time and effort, allowing them to focus on their core application.
Key Points
- 1Building a RAG pipeline involves chunking documents, embedding the chunks, storing them in a vector database, and retrieving relevant chunks at query time
- 2Implementing a fully functional RAG pipeline can be complex, especially when dealing with mixed document types, per-user document scoping, and switching between models
- 3Backboard provides a turnkey solution that abstracts away the underlying complexity of building a RAG pipeline, allowing users to focus on their core application
Details
The article explains that building a RAG pipeline from scratch is a multi-step process that involves chunking documents, embedding the chunks, storing them in a vector database, retrieving relevant chunks at query time, and injecting them into the language model. This process can become complex when dealing with mixed document types, per-user document scoping, and the need for both semantic and keyword search. Backboard simplifies this by handling all the underlying infrastructure, allowing users to focus on their core application. With Backboard, users can upload a document and have it queryable within minutes, without having to worry about the technical details of building and maintaining a RAG pipeline.
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