Enhancing Information Retrieval with Structured Data Processing

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The chapter discusses challenges faced by businesses in extracting information from unstructured textual data for language models, emphasizing the importance of structuring data through steps like cleaning and summarizing. It explores various chunking strategies to improve language model results, highlighting the impact of long context windows on document ranking and retrieval efficiency. Furthermore, it addresses the evolution of technology solutions for handling diverse document types and the importance of automated tools in streamlining data processing pipelines.

The majority of enterprise data exists in heterogenous formats such as HTML, PDF, PNG, and PowerPoint. However, large language models do best when trained with clean, curated data. This presents a major data cleaning challenge.

Unstructured is focused on extracting and transforming complex data to prepare it for vector databases and LLM frameworks.

Crag Wolfe is Head of Engineering and Matt Robinson is Head of Product at Unstructured. They join the podcast to talk about data cleaning in the LLM age.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .