This project involved successfully designing and deploying an AWS-based proof-of-concept (PoC) that demonstrated the efficacy of Large Language Model (LLM) inference in transforming unstructured data into structured documents. The objective was to process PDF documents through a well-coordinated AWS workflow, leveraging services such as Amazon Textract, AWS Lambdas, Amazon SageMaker, and AWS Step Functions.

The process initiated with PDF document uploads to an Amazon S3 bucket, triggering Textract to extract raw text. A Lambda function then preprocessed the data into manageable chunks. An LLM on SageMaker was utilised for real-time inference, activated during document processing and deactivated post-processing.

AWS Step Functions orchestrated the workflow, encompassing two crucial “map” methods. The first processed prompts from a document, while the second handled text chunks. A final reasoning Lambda extracted defining information from LLM outputs for each prompt and chunk. The resulting information was successfully transformed into the required formats for further processing by MRH Trowe.

Acknowledging the experimental nature of LLMs, the project’s primary deliverable was the AWS infrastructure, showcasing the LLM’s capabilities with unstructured data. The outlined solution provided a versatile framework, setting the stage for future developments, refinements and use cases, beyond the PoC stage.