Supporting Context-Aware AI-Augmented Data Extraction Features for Industrial Technical Documents

Leveraging AI/GenAI and data processing techniques to extract data from technical documents has proliferated due to recent advances in LLM capabilities and open source tools. However, being able to contextualize suitable GenAI/LLMs capabilities coupled with enterprise constraints on cost, data regulation, and quality is still challenging. Especially, AI/GenAI resource-constrained enterprises must deal with complex domainspecific technical documents of assets and designs supplied by multiple vendors. This paper presents novel practical methods that incorporate contexts into the design and execution of activities for industrial technical document extraction applications. We consider resource-constrained environments in which enterprises are with edge GenAI/LLMs infrastructures and non-AI engineers. We devise context-aware composition and adaptation for extraction pipelines to deal with diverse attributes of GenAI/LLMs and extraction quality control. We experiment our methods with technical documents for telco operators.