From PDF Archive to Study App: An LLM-Driven Data Pipeline
A lot of real-world problems boil down to the same shape: some scattered, unstructured source material exists (PDFs, archives, scanned documents), and what you actually need is a structured, usable product on the other end — categorized, searchable, and packaged for a specific audience. That transformation used to mean writing a pipeline by hand: a parser here, a classification script there, a templating layer to render the final output. With an LLM as a development partner, the same pipeline can be built through conversation — research, extraction, categorization, content generation and packaging, each step still a real, inspectable script or file, but designed and iterated on in natural language rather than written line by line. The part that changed the most for me wasn’t the coding — it was the analysis phase. Reading five years of exam PDFs, working out the syllabus scope, and deciding how to categorize dozens of tasks by topic and difficulty is the kind of manual review tha...