LF AI & Data: 'context is the new bottleneck' — open infrastructure Docling and DocLang

Context, not the model, becomes the limiting factor

Peter Staar, chair of the TAC (Technical Advisory Council) of the Linux Foundation AI & Data, makes a provocative argument: the model is no longer the bottleneck of modern AI systems — the bottleneck is the context layer, the layer that delivers relevant data and documents to the AI model as input context. RAG (Retrieval-Augmented Generation) and agentic systems face this problem daily — without robust infrastructure for document processing, even the most advanced model cannot produce quality answers. The comparison is clear: a better GPU doesn’t help if the model receives poorly prepared or unstructured data.

Which open projects address this challenge?

Two projects within the LF AI & Data ecosystem directly address the context layer problem. Docling covers document understanding — parsing, structuring, and semantic extraction from various formats. DocLang handles semantic interoperability, i.e. aligning meaning across heterogeneous systems. Both projects are open-source and licensed under the LF AI & Data umbrella, making them suitable for enterprise adoption without vendor lock-in. For comparison, commercial equivalents like Azure Document Intelligence or AWS Textract require cloud dependency and per-page pricing.

Three strategic priorities of the LF AI & Data community

Staar’s post outlines three development directions: first, growing community focus on context technologies as a separate discipline; second, deeper integration of Docling and DocLang through the broader Linux Foundation project ecosystem; third, lowering contribution barriers to attract more developers outside the narrow circle of research institutions. The initiative comes at a time when industrial demand for reliable document AI infrastructure is accelerating rapidly, and open projects lag behind commercial offerings in maturity and documentation.