Unlocking the power of unstructured data with RAG

Unstructured data, such as emails, audio files, or code comments, can provide valuable insights for developers and IT leaders, but it can be difficult to extract and interpret this information. Large language models (LLMs) and retrieval-augmented generation (RAG) can help interpret unstructured data, allowing for improved understanding of codebases, faster onboarding for junior developers, and speedier resolution of live site incidents. GitHub data scientists highlight that unstructured data can also enhance product decisions by revealing user pain points. LLMs like RAG can process unstructured data in a manner that is customized to a company's proprietary data and best practices.