The Best Way to Use AI for Learning (medium.com)

An experienced learner argues that AI should be used not merely to accelerate shallow consumption but to make sustained, hard learning feasible — letting professionals engage directly with primary, textbook-grade sources they’d previously have skipped. Using Christopher Bishop’s Pattern Recognition and Machine Learning as a demo, the author shows how AI can convert a daunting 710‑page text into manageable study materials (he focuses on the first 32 pages), enabling deeper understanding rather than more breadth. The piece reframes the value of AI in learning: it’s not just about speed, it’s about enabling study of more complex, abstract topics and protecting against misinformation by prioritizing primary sources over curated summaries. Technically, the workflow combines a high-quality PDF parser with OCR, an AI model (with large-context support), and a digital whiteboard for note cards. Parsing is critical because many models otherwise rely on imperfect RAG heuristics; explicit parsing lets you force precise page- or paragraph-level context. For long works enable “MAX” mode to exploit modern models’ huge context windows (roughly 400k–1M tokens, ~300k–750k words). The practical steps: parse the PDF, auto-generate translations/summaries/definitions tailored to your needs, place content as cards on a whiteboard (chapter or section per card), then iteratively read and discuss with the model. This card-based, spatial “giant desk” approach preserves flow, makes cross-referencing instant, and generalizes across domains to democratize access to advanced learning.