Summary of "Why you should take notes if you use AI"

Core message

If you use AI (large language models) regularly, you need a personal note‑taking system now. The shift is from prompt engineering toward context engineering: the quality of AI output — and the quality of your thinking — hinge on the context (your notes) you give the model.

Notes convert tacit knowledge (what’s only in your head) into explicit knowledge (documents the AI can read). That conversion is essential for clear communication, collaboration, and for getting AI to produce useful, non‑generic results.
Without explicit context, AI outputs will be generic, less helpful, and may encourage outsourcing thinking. With well‑organized notes, AI can summarize, spot patterns, synthesize, and help you think more deeply.

What to include in your notes — practical checklist

Prompt‑style basics

These are the common items people already think about when prompting an LLM:

Role: the persona/role the LLM should adopt (e.g., “career designer”, “copywriter”).
Goal: the objective for the output (e.g., “design a career path”, “write a sales letter”).
Audience: who will use/read the output (demographics, expertise, tone requirements).
Style/format constraints: desired length, headings, tone, formatting, channel specifics.

Context‑engineering items (often omitted but crucial)

These items make the model’s output aligned with your reality and priorities:

Inputs: explicitly state what data the LLM may use (for example, “use everything in my Obsidian vault except notes tagged #private”).
Source of truth: declare which documents/sources outrank others (official documents, canonical notes) and mark what should not be used.
Judgment: criteria or frameworks that define “good” vs “bad” for your outputs (evaluation rubrics, success metrics, preferred frameworks).

How to give the model useful judgment

Provide explicit frameworks so the LLM can evaluate outputs according to your standards rather than defaulting to generic templates.
If you don’t already have frameworks, give the LLM examples:
- Provide paired examples of “good” and “bad” outputs (the speaker suggests around ten pairs).
- Ask the LLM to distill the underlying framework or rules from those examples.
- Use the distilled framework as part of future prompts/context.

Recommended workflow — how to use notes with LLMs

Habit: make note‑taking automatic so context is already downloadable when you engage the LLM (low‑friction “drag & drop” context).
Store connected notes (linked notes, a vault) so the model can find and compound on core ideas over time.

When interacting with an LLM, include:

Role / goal / audience / style constraints.
Specification of allowed inputs and excluded tags/documents.
Attachment or marking of source‑of‑truth documents (priority documents).
The judgment framework, either provided or derived by the LLM.

Use the LLM to:

Synthesize and summarize large sets of your notes.
Extract themes and patterns you may have missed.
Suggest resources, authors, books, and concrete next steps aligned with your documented interests.
Prototype career or project pathways grounded in your own material.

Lessons from the demo (practical evidence)

Generic prompts in an incognito ChatGPT session produced high‑level, generic career suggestions that felt bland and unguiding.
Feeding a model (Claude via Claude Code) the speaker’s Obsidian vault produced richer, more insightful synthesis: it clarified core ideas, revealed patterns the speaker hadn’t explicitly stated, and suggested targeted readings and career positions aligned with the speaker’s documented interests.
The difference demonstrates that the same LLM capability yields much better, personally useful outputs when fed personal context and explicit evaluation frameworks.

Benefits and warnings

Benefits:
- Better quality outputs from LLMs.
- Deeper engagement with problems and stronger insight synthesis.
- Faster discovery of productive directions and idea compounding across time.
Warnings:
- If you only consume AI outputs and never document/process your own thinking, you risk outsourcing cognition and becoming less engaged.
- The antidote is explicit note‑taking and feeding that context to the LLM.

Practical tool notes and next steps

Tools mentioned as useful:
- Obsidian (personal vault/notes)
- Claude / Claude Code (contextual LLM session)
- ChatGPT / Gemini (examples of LLMs)
- Elicit or “research mode” for academic or deeper sourcing
The speaker indicates setup instructions (e.g., connecting a vault to Claude/Claude Code) are available in the video description.

Suggested immediate actions:

Start a note system (for example, Obsidian) if you don’t have one.
Document role/goal/audience/style templates.
Tag and mark source‑of‑truth documents and privacy/exclude tags.
Add examples of good/bad outputs or the frameworks you currently use.
Run an LLM pass on your vault to synthesize and surface themes; iterate from the results.

Speakers and sources referenced

Primary speaker: the video’s host / channel creator (unnamed in the subtitles).
AI models/tools referenced: ChatGPT, Claude (Anthropic), Claude Code, Gemini.
Note/storage tool: Obsidian.
Research tools/methods referenced: Elicit; “research mode” (generic).
People and concepts referenced: Maslow (Maslow’s hierarchy), Alfred North Whitehead (process philosophy), unspecified Nobel laureate studies, and studies about cognitive effects of outsourcing thinking.
Other references: “this channel” / “another video” (same creator’s content).