A new pipeline uses chained LLM passes, not a single prompt, to pull complete specs out of hardware datasheets.
Researchers built an automated pipeline that turns hardware datasheets into hierarchical knowledge graphs. Instead of feeding a whole datasheet to an LLM in one shot, the system breaks documents into chunks, extracts properties from each piece, routes them into a hierarchy, and refines the graph iteratively while keeping a record of where each property came from. The team tested the approach on 20 datasheets covering different component types and document lengths. Against a one-shot baseline, the pipeline hit 98.8% property faithfulness versus 76.1%, and it captured 83.6% of everything the baseline got right, while the baseline captured only 10.5% of what the pipeline found.
The real finding here isn't that LLMs can read a datasheet. It's confirmation that dumping a whole document into a big context window and asking for all the specs quietly drops information, even when the document fits inside the window. That's a meaningful caveat for anyone building retrieval or search tools on top of long technical documents, not just hardware engineers.
Datasheets are the kind of unglamorous, high-stakes text nobody wants to proofread by hand, which is exactly why automating it carefully, rather than quickly, is the right call.