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.
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.
Prompt Engineers and Their Role
The role of prompt engineers is developing as integral to the data processing landscape, where they leverage their domain knowledge to create effective prompts for data extraction. These engineers are tasked with conceptualizing the JSON outputs needed from various unstructured documents, simplifying the transition from raw data to structured formats. As organizations begin to embrace this emerging role, it becomes apparent that prompt engineers may initially overlap with existing data engineering roles until the former becomes established. The use of prompt engineers is critical for reimagining data processing tasks traditionally handled by data engineers, especially in handling unstructured data.
Ensuring Reliable Data Extraction
Unstrapped employs a methodology known as LLM Challenge to ensure accurate data extraction from unstructured documents. By utilizing multiple LLMs from different vendors, the system extracts information and then verifies it through consensus, allowing for greater accuracy and reliability. If consensus is not achieved, the system opts for null values rather than incorrect data, thus preserving the integrity of the information extracted. This approach not only enhances data reliability but also mitigates the trappings of hallucinations common in single-model extractions.
Practical Applications and Future Modes
Unstrapped is particularly well-suited for processing uniform types of unstructured documents across various business verticals, especially in legal and financial sectors with high volumes of documentation. The platform functions effectively as a straightforward machine-to-machine interface, facilitating the transformation of data from unstructured formats directly into JSON for further machine consumption. Future developments leverage multimodal capabilities to potentially include chart and figure interpretation, expanding its analytical scope. However, the current focus remains on unstructured text extraction, a critical need across numerous industries.
Shuveb Hussain is co-founder of Unstract, a no-code platform that uses large language models to extract structured data from unstructured documents, allowing users to build API endpoints and ETL pipelines to automate document processing workflows.