Devs working with LLMs run into doc parsing continuously. And each few months, there’s a brand new wave of hype (or frustration) across the PDF drawback. Throughout these moments, it’s commonplace to see software program of us venting about how one file format turned such a large headache. However the battle isn’t new.
Lengthy earlier than LLMs entered the image, complete SaaS companies had been constructed round managing the messiness of PDFs. And for good cause, it’s a format that was by no means designed for the type of structured, machine-readable entry we now count on.
When software program turns into as widespread as Adobe Acrobat and the PDF format, it begins to really feel like a everlasting a part of the panorama. It’s simple to neglect that behind that ubiquity had been actual design choices, constraints, and tradeoffs made by actual engineers fixing actual issues. Issues that, over time advanced and have become the roots of in the present day’s ache.
Sure, PDFs are irritating. However they weren’t born damaged. In actual fact, they had been a surprisingly elegant answer for his or her time.
So let’s zoom out. This story takes a step again to discover the origins of the PDF format: the way it got here to be, what issues it got down to resolve, and the way the choices made within the early 90s nonetheless ripple via in the present day’s stack. The aim: to know not simply the “why is that this so arduous?”, but in addition the “how did we get right here?”
The shift had begun. Private computer systems had been exploding in recognition and paper paperwork had been now not the default. Software program like VisiCalc, WordStar, WordPerfect, and early Microsoft Phrase marked the daybreak of a brand new solution to write, edit, and share.
By the late ’80s, PC suites had all however killed off the typewriter. Executives may tweak experiences minutes earlier than a gathering. Analysts had been working “what-if” situations in spreadsheets. Lecturers had been printing checks on the fly. Engineers changed drafting tables with digital blueprints.
More and more, paperwork turned the brand new office. Not simply the tip product, however the place the work really occurred.
Within the early Nineteen Nineties, the rise of PC-based phrase processing and digital file sharing solved many issues, whereas introducing new ones. Each laptop had its personal fonts, printer drivers, and format quirks. A report that appeared good on one machine may print as a jumbled mess on one other. Sharing recordsdata turned of venture.
To repair this, in 1991 Adobe co-founder John Warnock and his staff launched a undertaking codenamed “Camelot” to create a really common doc format. The outcome was the PDF, a file that embedded fonts, graphics, and web page format multi function place. This “digital paper” assured that paperwork appeared precisely the identical all over the place, whether or not on Home windows, Mac, or any printer.
By bundling each font, picture, and format element right into a single file, PDFs let customers share paperwork with out surprises, what you noticed on display printed precisely the identical all over the place. Adobe made the free Acrobat Reader obtainable in 1994, and inside 5 years, PDF turned the go-to format for all the pieces from product manuals and company experiences to authorities types and educational papers. By the early 2000s, “export as PDF” was a one-click choice in nearly each authoring instrument, and organizations throughout industries embraced it for distribution, archiving, and compliance. And it’s nonetheless the usual in the present day.
The very factor that made PDFs so interesting (their promise of pixel-perfect constancy) additionally launched a hidden trade-off: it locked content material right into a inflexible, print-first construction.
Beneath each flawless web page was basically a digital snapshot, constructed to imitate what got here out of a printer. Headings, tables, paragraphs, none of it had semantic that means. To a pc, it was simply coordinates and textual content bins scattered throughout a canvas.
At first, this didn’t matter. However as paperwork moved from desktops to net browsers, cell screens, and automatic pipelines, the cracks started to indicate. Need to extract clear knowledge? Reflow textual content on a cellphone? Perceive doc construction? Instantly, what appeared clear to people turned a multitude for machines.
Adobe wasn’t blind to the issue. Tagged PDF (launched in 2001 and later formalized in PDF/UA for accessibility) provides an HTML-like logical construction. It by no means turned common, however it’s mandated for accessible authorities paperwork and broadly utilized in large-enterprise workflows. Different milestones, akin to PDF/A for long-term archiving, XMP metadata help, and the 2008 hand-off of the spec to ISO, present regular efforts to modernize the format. Nonetheless, broad adoption lagged; tagging is invisible to most customers, tedious for creators, and infrequently stripped out by careless export settings.
An entire ecosystem of SaaS instruments popped as much as bridge this hole. You see it in heavyweights like DocuSign, within the many web-based PDF editors akin to DocHub, and in open-source libraries like Poppler, which builders rely upon simply to tug textual content out of PDFs. That’s additionally why the large cloud gamers are all throwing critical AI muscle at this drawback: AWS with Textract, Google with Doc AI, and Microsoft with Azure AI Doc Intelligence. The market emerged, merchandise adopted, and loads of income flowed. Adobe, whether or not we prefer it or not, modified the sport.
When ChatGPT hit, the “PDF drawback” exploded. Corporations scrambled to feed their knowledge into LLMs, solely to hit a wall: most of that invaluable information was locked away inside PDFs.
At first, the aim was easy: simply extract clear textual content for Retrieval-Augmented Era (RAG). However that shortly proved too primary. With out format consciousness, textual content from columns acquired scrambled, tables changed into nonsense, photographs acquired ignored, and essential context disappeared.
Trendy Doc AI now trains fashions to know a doc’s visible and logical format: figuring out titles, paragraphs, tables, and pictures. So AI can reference data, skip repeated headers/footers, and grasp the general construction.
This AI stack reveals the total extent of the mess we’re coping with. What needs to be simple knowledge extraction now requires a number of specialised layers:
- Format evaluation to know doc construction,
- OCR to extract textual content from photographs and scanned paperwork,
- VLM orchestration to coordinate these completely different AI parts.
Every layer provides latency, potential errors, and compute value. The irony is staggering: we’re utilizing a number of the most superior AI fashions ever constructed to resolve an issue that stems from a 30-year-old choice to deal with paperwork like images.
Whereas PDFs have steadily advanced, their print-first DNA retains piling prices onto each fashionable workflow. Structured codecs, scanned or photographed, do introduce a number of the identical hurdles, however PDF’s design amplifies the ache.
We will’t scrap many years of PDFs in a single day, however we will keep away from repeating historical past. For brand new content material, select born-digital codecs that protect semantics by default:
- HTML5 for the online,
- Markdown-derived requirements for technical docs,
- or DOCX/OOXML when Workplace compatibility is a should.
When a fixed-layout file is unavoidable, export with full tags and metadata intact; some authoring instruments now automate this. Authorities procurement guidelines that require PDF/UA compliance are a optimistic precedent. Comparable strain from enterprises on distributors and regulators can push tagging from “nice-to-have” to “desk stakes.”
Long run, open requirements like W3C’s Transportable Net Publication or EPUB 3, together with upcoming containerized JSON-based codecs, promise constancy with out sacrificing construction. Supporting these in mainstream authoring instruments (and educating customers to undertake them) will spare the subsequent era from writing imaginative and prescient fashions simply to tug textual content out of a contract.
The story of PDFs proves that early design selections echo for many years. The lesson isn’t to vilify the engineers who solved 1991’s drawback; it’s to acknowledge that in the present day’s “ok” shortcuts turn into tomorrow’s expensive handcuffs. Let’s embed semantics on the supply, again open, machine-readable requirements, and make sure the subsequent wave of doc tech is constructed for people and machines alike.
For groups already coping with legacy codecs, instruments like Chunkr supply an Open-Supply API-based pipeline to transform complicated paperwork into structured, chunked codecs tailor-made for LLM and RAG workflows, obtainable each as hosted endpoints or self-managed infrastructure.
