A modest proposal: Reformat everything to make documents more palatable to AI
The LF AI & Data Foundation has launched DocLang, a standardized XML-based format designed to help AI models parse document structure without losing semantic context.
Deep dive
- DocLang uses a limited XML vocabulary mapped 1-to-1 to LLM tokens.
- It maintains structural relationships, tables, and provenance that are often lost in PDF extraction.
- Projects like IBM's Docling are intended to act as the conversion layer for this new standard.
- Reduces token usage by providing structured metadata instead of forcing models to interpret raw visual layouts.
- Targeted at replacing brittle, one-off custom parsers currently used in enterprise pipelines.
Decoder
- OCR: Optical Character Recognition; software that converts images of text into machine-readable characters.
- Tokenizers: Components that break down text into individual units (tokens) for LLM processing; efficient token usage directly correlates to lower inference costs.
Original article
A modest proposal: Reformat everything to make documents more palatable to AI
Websites are being redesigned for consumption by AI models, and now a coalition wants to extend the trend to digital documents.
The LF AI & Data Foundation, under the Linux Foundation, has formed a working group to steer the development of DocLang, an AI-friendly document format that aims to help enterprises feed their files to AI systems.
The DocLang group, founded by IBM, NVIDIA, Red Hat, ABBYY, HumanSignal, and Forgis, contends that existing formats like PDF, Markdown, HTML, and LaTeX are ill-suited for AI document parsing.
In late 2024, IBM developed an open source toolkit called Docling to facilitate AI document parsing, not unlike Microsoft's MarkItDown or the Marker project. Docling provides a way to convert various file formats into structured AI-ready data. DocLang expands upon that foundation with a standard for exchanging structured output across different systems.
"DocLang is designed to solve one of the foundational problems in enterprise AI: documents were built for humans, not machines," said Maxime Vermeir, VP of AI Strategy at AI automation biz ABBYY in a statement. "By introducing a minimal, standardized, and AI-native representation of document structure, layout, meaning and governance, DocLang creates a far more deterministic foundation for modern AI systems."
The new DocLang format is necessary, the spec authors argue, because existing formats were designed for rendering and lose semantic information, structural relationships, or geometric context when AI models turn them into tokens. The specification explains that Markdown lacks sufficient scope, that HTML is excessively verbose, and that LaTeX allows too much ambiguity.
Essentially, DocLang is optimized for LLM tokenizers through markup that maps between DocLang elements and LLM tokens on a 1-to-1 basis. The spec relies on a limited XML vocabulary that aligns with LLM tokenizers to produce optimized prompts. It is lossless, so the AI conversion doesn't do away with valuable info. It's designed to support common graphical elements like tables, formulas, charts, and multimodal content. And it's an open standard.
DocLang could also help keep costs under control. According to AI Cost Check, having an AI model conduct an OCR scan on a PDF requires about 1,200 input tokens and 150 output tokens as a baseline.
That's inconsequential to corporate AI customers on a one-off basis but demands attention at scale. And because AI models have highly variable token costs, companies may find they are spending more than they anticipated to have their AI system ingest PDFs, particularly if the documents are long and complicated or an expensive frontier model is used.
"PDFs were designed for rendering, not understanding," said Jon Knisley, AI Value and Enablement Lead at ABBYY, in an email to The Register. "Every time a PDF enters an AI pipeline, structure, meaning and layout get lost, so the model's accuracy ends up bottlenecked by document quality rather than model quality. Teams compensate by building custom parsers at every integration point, which results in brittle, one-off work, and a new engineering sprint for every new document type."
According to Knisley, that has measurable cost.
"Ambiguous structure forces the model into guesswork, which drives up hallucination risk and burns tokens deciphering layout instead of extracting meaning," he explained. "With DocLang, customers can expect better accuracy, lower costs, fewer tokens consumed, faster performance and more consistent outputs. The exact savings depend on the use case and document complexity, but our initial benchmarks show 4x to more than 30x lower cost depending on the model evaluated."
Knisley also cited governance advantages, noting that document provenance data and metadata can get stripped when documents gets moved. DocLang, he said, keeps that information attached.
ABBYY, which offers AI document processing, has created the DocLang Interactive Benchmark to illustrate the potential token savings of feeding DocLang documents to AI models. A PDF of IBM's 2025 annual report, for example, results 8,421 input tokens and 512 output tokens while a DocLang version requires only 5,310 input tokens and 498 output tokens. What's more, the DocLang version results in lower latency (2.7s vs 4.2s) and delivers better quality (the AI missed one subsection and mangled a table merger in the PDF).
"It's still early, and we won't overstate adoption," said Knisley. "The standard is open and free to build on, and the group is actively inviting more technology providers and enterprises to join. The early response has been encouraging, and we're optimistic about where it goes from here."
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