Supercharging Your Learning with AI for Better Notes And Learning with ChatGPT and Obsidian

AI can turn “information overwhelm” into a structured learning pipeline by filtering what’s worth consuming, translating confusing ideas into your preferred mental model, and converting highlights into connectable conceptual notes inside Obsidian. The core workflow starts before reading or watching finishes: paste transcripts (or raw notes) into ChatGPT with prompts that produce key takeaways, then further narrow those takeaways using your own goals and interests—so clickbait and irrelevant sections get screened out early.

A practical example centers on deciding whether to watch a YouTube video. Instead of relying on a vague title, the workflow copies the transcript from the YouTube interface and asks ChatGPT for a detailed, dash-formatted list of key takeaways. That list becomes a “holistic view” that helps the learner judge relevance immediately. The prompts can then be customized to exclude categories like sponsorships and product placements, and—more importantly—to filter content against a personal objective. In the transcript, the objective is finding a meaningful, fulfilling life tied to “living a life that is difficult to explain” and the concept of the self. ChatGPT narrows the material to only what matches those criteria, leaving out everything else.

The same goal-based filtering extends to curiosity-driven learning. The workflow uses a set of “12 favorite problems,” open-ended questions the learner wants to answer. ChatGPT is asked to check each problem against the content and, for each problem, list the most relevant ideas—or explicitly report “no direct relevance” when nothing fits. This turns passive consumption into targeted research: the learner can decide what to dive into next based on which problems actually connect.

Once relevant material is identified, AI acts as a personalized tutor for understanding. When a paragraph or concept is unclear, the learner asks ChatGPT to explain it from different angles—such as “as if I was a gamer” when studying mental models and systems thinking. The transcript emphasizes iterative questioning: if the first explanation still doesn’t land, follow-up prompts can keep refining the explanation until it clicks. AI can also reframe explanations for different audiences (e.g., a five-year-old) or for first-principles reasoning.

After comprehension comes note transformation. Raw highlights often need cleanup—punctuation, grammar, and sometimes deduplication—so ChatGPT is used as a “formatting slave” while preserving each highlight’s link. Then the workflow converts highlights into conceptual notes without drowning in volume. It recommends progressive summarization: start with general summaries and themes, then organize key takeaways under higher-order topics, and optionally extract only the most important parts (e.g., bolded headers) to keep the conceptual notes atomic and connectable.

Finally, the system focuses on building a web of connections in Obsidian. It uses AI to generate new note titles and to creatively connect new notes to existing ones (e.g., linking non-dual mindfulness ideas to a self-actualization note). For discovery inside a vault, it leverages the Smart Connections plugin to surface relevant existing notes even when terminology varies. When connections are too broad, it proposes creating bridging links using FEI Ling seng’s “Compass” framework: North links (sources/parents), West links (similar/contrasting ideas), and South links (what the note causes). The result is a repeatable method for turning scattered highlights into a structured, searchable knowledge network.