Documents to LLM‑Ready Inputs
Real data is messy — PDFs, web pages, images, scanned paper. Turning all of that into clean text a model can actually use is half the work of grounding.
The Prep Pipeline
Every document goes through the same path before it can ground an answer:
- Extract — pull the text out of PDFs, HTML, and images
- Clean — strip junk and boilerplate, fix broken encoding
- Chunk — split it into retrievable pieces
- Tag — keep the source and metadata with each piece
Why Chunking Matters
Chunk size is a balance. Too big wastes the context window and blurs what is relevant; too small loses the meaning around each piece.
Keep the Source
Attach to each chunk exactly where it came from — the file, the page, the section. That is what lets an answer cite its evidence later.
- Traceability — every claim can point back to a real passage
- Honesty — this is what Module 8 leans on to show its work
- Debugging — when an answer is wrong, you can find the chunk that caused it
Garbage In, Garbage Out
Bad extraction poisons everything downstream — scrambled PDF text, leftover navigation menus, and boilerplate all get retrieved as if they were real content.
Build It
How to implement: take one messy document, extract its text, and split it into a few sensible chunks — putting a source tag on each one.
- Weekly AI Tasks tracker — incoming messages are short and clean, but the moment you ingest notes or attachments, they will need this pipeline.
- Personal brand site — extract clean facts from a résumé and repos, and tag each with its source so the site can cite them.
What you learned
Documents become LLM‑ready in four steps — extract, clean, chunk, tag. Chunk on natural boundaries, keep the source with every piece, and remember: garbage in, garbage out.