Blog

A practical way to check whether a PDF contains text that blends into the page, sits off-page, or remains invisible before you send it to AI.

A practical checklist for deciding what to remove, check, and ask before uploading a sensitive PDF to an AI service.

A practical review pass for checking AI summaries against the source PDF before you rely on them.

Five practical levers to lower LLM API spend on PDF workloads, from model choice to noise trimming and smart chunking.

How LLM API billing works for PDFs, a worked cost example for a batch job, and why token estimates go wrong.

Page, paragraph, or heading: which chunking mode works best for your PDF and retrieval pipeline, with overlap guidelines.

A step-by-step workflow to clean, convert, measure, and chunk a PDF before feeding it to a retrieval pipeline.

Context windows, page estimates, tokenizer differences, and a practical rule for when to chunk versus send the full file.

A practical page estimate for model context windows, and why tokens still matter more than page count.

How silent truncation happens with long PDFs, why it is risky, and a practical workflow to prevent missing sections.

Why word count alone misleads AI prompt planning, and how PDF structure can inflate tokens far beyond expectation.

Embedded files, JavaScript, and hidden annotations travel in PDFs without appearing on any page. Why this matters when you share documents or upload them to AI tools.