How to Turn Documents Into AI-Readable Data
Learn why real documents need cleaning before AI can use them, and what the cleaning involves.
The lessons so far quietly assumed your documents were neat paragraphs of text. Real company knowledge looks nothing like that. It lives in PDFs with two columns and page numbers, scanned letters, web pages wrapped in menus and cookie banners, slide decks, and spreadsheets full of numbers.
Feed those in naively and you get rubbish. A two-column PDF read line by line interleaves the columns into nonsense. A scanned page is just a picture, so it contains no text at all until OCR (optical character recognition) reads it, occasionally misreading a letter. A web page arrives with “Home | About | Accept all cookies” in the middle of the article. A table turns into a jumble of numbers with no idea which column they belonged to.
The rule of every data pipeline applies with full force here: garbage in, garbage out. If the text in your index is mangled, the embeddings are mangled, the search fails, and the model gets nonsense to read. Many disappointing RAG systems turn out to have a perfectly good model and a broken first step.
Turning documents into AI-ready text usually involves:
- Extracting text in reading order, using OCR where there is only an image.
- Removing clutter: headers, footers, page numbers, navigation, adverts.
- Keeping structure: headings, lists, and tables with each value still linked to its column name.
- Recording metadata: source, page, date, author, so answers can be cited and filtered.
It’s unglamorous work, and it’s often where the biggest quality gains are hiding.
Key takeaways
- Real documents are messy: multi-column PDFs, scans, web pages with clutter, tables.
- Naive extraction garbles them, and garbled text means failed search and wrong answers.
- Cleaning means reading order, OCR for images, removing clutter, and keeping headings and table structure.
- Add metadata (source, page, date) so answers can be cited and filtered.
Check your understanding
Go deeper (more technical)
Tools range from simple PDF text extractors to layout-aware parsers and vision-language models that “look” at a page and describe its structure, including charts and tables. Converting documents to Markdown is a popular intermediate format, because headings and tables survive in a form models read well.
Images and diagrams can be handled too: a vision model writes a description of each figure, and that description is embedded like any other text (Track 8 covers how models see).