The Data Exchange with Ben Lorica cover image

The Data Exchange with Ben Lorica

Automating Unstructured Data Extraction with LLMs

Aug 8, 2024
Shuveb Hussain, co-founder of Unstract, discusses his innovative no-code platform that automates the extraction of structured data from unstructured documents. He highlights the rise of prompt engineers and their role in data transformation. The conversation dives into the complexities of using large language models and the critical importance of quality optical character recognition. Hussain also addresses the fine-tuning of language models for specific needs and the integration of diverse document types, showcasing how these advancements enhance data processing efficiency.
35:29

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Unstruct uses large language models to automate the extraction of structured data from unstructured documents, streamlining data processing workflows significantly.
  • The emergence of prompt engineers is crucial for optimizing data extraction tasks, bridging the gap between raw unstructured data and structured formats effectively.

Deep dives

Inspiration behind Unstrapped's Creation

Unstrapped was inspired by the emergence of large language models (LLMs) capable of reasoning and instruction following, recognizing the need to structure unstructured data effectively. Initially focused on structured data, the founders saw an opportunity to tackle the pervasive issue of unstructured document processing using LLMs. The solution targets the automation of data extraction from unstructured documents, providing a streamlined platform for this purpose. This necessity to automate and streamline data processing was a significant driving force for the founding of the company.

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