I Built a RAG Pipeline From Scratch. Here’s What I Learned About Unstructured Data.

Building a Retrieval Augmented Generation (RAG) pipeline from scratch offers valuable insights into the management of unstructured data and the potential of machine learning, despite initial perceptions of complexity. The process involves cleaning and preprocessing vast amounts of unstructured data, which comprises a significant portion of global data, and then constructing a retriever to identify relevant information and a generator to produce accurate responses. While the task demands an understanding of data engineering, machine learning, and natural language processing, it reveals the transformative power and competitive advantages of effectively leveraging unstructured data. The journey of building a RAG pipeline highlights the importance of continuous learning and optimization, as each component—from data cleanliness to the performance of the retriever and generator—can be refined to enhance the pipeline's output and reliability.