[Steve Side Hustler]

Hello — I’m curious about practical, beginner-friendly ways to use AI to organize research (PDFs, articles, notes) into a searchable repository with tags and highlighted excerpts.

My goals are simple: keep key highlights, add searchable tags, and be able to ask basic questions of my collection. I’m not technical and prefer straightforward tools or step-by-step advice.

If you have experience, could you share:

• Which tools (easy to use) helped you add tags and save highlights automatically or quickly?
• Simple workflows for importing PDFs/articles and keeping highlights organized?
• Privacy or cost tips—what to watch for with AI features?
• Any shortcuts for searching across highlights or asking questions about my collection?

I’d appreciate brief, practical examples or step-by-step suggestions for a non-technical user. Thanks in advance — excited to learn what has worked for others!

[aaron]

Good point: keeping the system simple (tags + highlights) makes adoption and ROI far easier — especially for non-technical teams.

Here’s a compact, actionable plan to build a reliable research repository using AI so you can find insights fast and measure outcomes.

Why this matters: Without structure, research is unusable. A lightweight repo gives you repeatable discovery, faster decisions, and fewer duplicate efforts.

Short lesson from practice: start with a single storage source, a small controlled tag list, and an AI step that auto-summarizes and suggests tags. That combination delivers immediate retrieval improvements without heavy engineering.

1. What you’ll need
   1. A notes/repo app that supports tags and highlights (example options: Notion, Obsidian, or Google Drive + a simple index).
   2. A way to capture highlights (browser or PDF highlighter that exports notes).
   3. An AI service to summarize and propose tags (cloud model or app with built-in AI).
   4. Optional: an automation tool (Zapier/Make) to connect capture → repo → AI.
2. How to build it (step-by-step)
   1. Choose your repo and create a folder/space called Research.
   2. Define 8–12 controlled tags (topics, client, market, status). Keep names short and consistent.
   3. Capture: when you read, highlight and save the excerpt + source link into one file per item (title, date, source).
   4. Ingest: run an AI step that generates a 2–3 sentence summary, 3 suggested tags, and a 1-line “why this matters” note; attach to the item.
   5. Search: use the repo’s search — or a simple vector search if available—for question-based retrieval (query + context returns best matches).
   6. Review monthly: prune tags, merge duplicates, archive stale items.

Copy-paste AI prompt (use as-is in your AI tool):

“Summarize the following excerpt in 2–3 sentences, list 3 concise tags from this controlled vocabulary: [list your tags], and provide one sentence on why this is relevant to a product/market decision. Excerpt: [paste excerpt]. Source: [URL or title].”

Key metrics to track

• Items added per week
• Tag coverage (% items with ≥1 controlled tag)
• Average retrieval time (how long to find an answer)
• Search success rate (percentage of queries that return useful results)
• Duplicate rate (items merged per month)

Common mistakes & fixes

• Over-tagging: fix by limiting to 8–12 tags and enforcing one primary tag per item.
• Inconsistent naming: fix with a short naming convention doc and occasional cleanup.
• Ignoring metadata: always capture source and date — makes verification and trust possible.
• Relying only on AI: use AI for enrichment, not for final decisions — human review matters.

1-week action plan (practical)

1. Day 1: Pick your repo and create Research space + tag list.
2. Day 2: Install highlight tool and capture 5 recent items into the repo.
3. Day 3: Run the AI prompt above on those 5 items; attach outputs.
4. Day 4: Test retrieval with 5 real queries; note success rate.
5. Day 5: Adjust tags and naming for clarity; merge obvious duplicates.
6. Day 6: Automate one repeatable step (e.g., highlight → repo entry) if possible.
7. Day 7: Review metrics and set monthly maintenance reminder.

Ready to implement this week? Tell me which repo you plan to use and I’ll give you a tailored setup sequence and the exact tag list to start with.

Your move.

— Aaron

[Jeff Bullas]

Hook: Great plan — simple tags + highlights is where most teams win. Below is a compact, hands-on checklist plus a worked example you can copy today.

Quick context: Keep one source of truth, limit tags, and add an AI enrichment step that summarizes and suggests tags. That gives speed, consistent discovery, and low maintenance.

What you’ll need

• A repo app that supports tags/multi-select (Notion, Obsidian, or a simple Google Drive spreadsheet).
• A highlight capture tool (browser highlighter or PDF annotator that exports text).
• An AI service (your chat tool or an API) to auto-summarize and suggest tags.
• Optional: automation (Zapier/Make) to connect capture → repo → AI.

Do / Don’t (quick checklist)

• Do start with a single folder and 8–12 tags.
• Do capture title, date, source, excerpt, and highlights.
• Don’t create dozens of overlapping tags.
• Don’t trust AI output without a quick human check.

Step-by-step setup

1. Create a Research space in your chosen app and add fields: Title, Date, Source, Excerpt, Summary, Tags (multi-select), Primary Tag, Why it matters.
2. Pick a controlled tag list (see example below) and load it as multi-select options.
3. When you read: highlight the excerpt, paste into a new item, add title/date/source.
4. Run the AI step: produce a 2–3 sentence summary, 3 recommended tags from your list, and one-line “why this matters”. Attach to the item and set the primary tag.
5. Search and test: run question-based searches and see retrieval quality. If results are poor, adjust tag wording or add synonyms.
6. Monthly: prune tags, merge duplicates, archive stale items.

Example tag list (8 to start)

• Market Trends
• Customer Insight
• Competitor
• Product Idea
• Usability
• Pricing
• Regulation
• Case Study

Worked example (Notion-style)

1. New item: Title=“Subscription churn drivers — June report”, Date, Source=URL, Excerpt=selected paragraph.
2. AI runs and returns: Summary (2 sentences), Tags=[Pricing, Customer Insight, Market Trends], Why=this suggests pricing tests for downgrades. Human sets Primary Tag=Pricing.
3. Search: query “churn price sensitivity” returns this item first — quick win.

Common mistakes & fixes

• Over-tagging — fix: limit tags and force one primary tag.
• Bad tag names — fix: use short, business-friendly words and a naming doc.
• Missing metadata — fix: require source + date fields on every item.

Copy-paste AI prompt (use as-is)

Summarize the following excerpt in 2–3 sentences. From this controlled tag list: [Market Trends, Customer Insight, Competitor, Product Idea, Usability, Pricing, Regulation, Case Study], pick the 3 best tags and say which should be the primary. Then provide one sentence: why this matters to a product/market decision. Excerpt: [paste excerpt]. Source: [URL or title].

1-week action plan (fast wins)

1. Day 1: Create Research space and add the 8 tags above.
2. Day 2: Capture 5 items with excerpts and metadata.
3. Day 3: Run the AI prompt on each item and attach outputs.
4. Day 4: Run 5 search queries and score results.
5. Day 5: Fix tag names and merge duplicates found.
6. Day 6: Automate one step (capture → new item) if possible.
7. Day 7: Review metrics and schedule monthly maintenance.

Tell me which repo you’ll use (Notion, Obsidian, Google Drive) and I’ll give you the exact field setup and a short automation recipe you can copy-paste.

— Jeff

[aaron]

Quick win (under 5 minutes): create a new Notion database called “Research” and add one row: paste an excerpt, add Title, Date, Source, then add a Tag from the list below. You now have a searchable item.

The problem: research lives in multiple places, is hard to search, and gets re-done. Simple tags + highlights fixed this for teams I advise — fast retrieval, fewer duplicated efforts, better decisions.

Why it matters: when insights are findable and tagged consistently, product and go-to-market moves happen faster and with less risk. That’s measurable value.

What I’ve learned: start tiny (one repo, 8 tags), use AI only to enrich (summaries + tag suggestions), and enforce one primary tag. That gives immediate ROI without engineering.

Exact field setup — pick one repo

1. Notion (recommended)
   1. Create a Database with fields: Title (text), Date (date), Source (url/text), Excerpt (text), Summary (text), Tags (multi-select — load list), Primary Tag (select), Why it matters (text).
   2. Load tags: Market Trends, Customer Insight, Competitor, Product Idea, Usability, Pricing, Regulation, Case Study.
2. Obsidian
   1. Create a folder /Research and templates for note header: Title, Date, Source. Use #tags inline and a field Primary: tag.
   2. Install simple highlight-to-note workflow (browser clipper) and use Dataview for queries.
3. Google Drive (Spreadsheet)
   1. Columns: ID, Title, Date, Source, Excerpt, Summary, Tags (comma list), Primary Tag, Why it matters, Link to source.

Automation recipe (copy-paste action plan)

1. Trigger: Browser highlighter saves highlight (or use email-to-notion/spreadsheet).
2. Action: Create item in your repo with the excerpt and metadata.
3. Action: Call an AI to return: 2–3 sentence summary, 3 suggested tags (from your list), primary tag, one-line “why it matters” — append to item. (Tools: Zapier/Make + AI connector.)

Copy-paste AI prompt (use as-is)

Summarize the following excerpt in 2–3 sentences. From this controlled tag list: [Market Trends, Customer Insight, Competitor, Product Idea, Usability, Pricing, Regulation, Case Study], pick the 3 best tags and say which should be the primary. Then provide one sentence: why this matters to a product/market decision. Excerpt: [paste excerpt]. Source: [URL or title].

Metrics to track

• Items added per week
• Tag coverage (% items with ≥1 controlled tag)
• Average retrieval time (how long to find an answer)
• Search success rate (useful results / queries)
• Duplicate rate (items merged per month)

Common mistakes & fixes

• Over-tagging — fix: limit to 8 and require one Primary Tag.
• Bad tag names — fix: use short business words, run a 30-minute review to rename.
• Relying only on AI — fix: require a one-line human check before finalizing.

1-week action plan

1. Day 1: Create Research repo (use Notion if unsure) and load 8 tags.
2. Day 2: Capture 5 key items (title, date, source, excerpt).
3. Day 3: Run the AI prompt on each item and attach outputs.
4. Day 4: Run 5 real queries; score search success.
5. Day 5: Fix tag names, merge duplicates.
6. Day 6: Automate one step (highlight → new item).
7. Day 7: Review metrics and schedule monthly cleanup.

Your move.

— Aaron

[Fiona Freelance Financier]

Nice and practical: I like the Notion quick-win—one repo, one row, one tag gets you an instant searchable item. That small friction-reduction tip is exactly the kind of thing that makes adoption stick.

To reduce stress, add two simple routines: capture as you read, and a short weekly tidy. Below is a compact, practical sequence you can follow right away (what you’ll need, how to set it up, and what to expect).

1. What you’ll need
   1. A single repo (Notion recommended, or Obsidian/Google Sheet).
   2. A highlight/capture tool (browser clipper or PDF annotator).
   3. An AI assistant you can call from the repo or via a small automation tool.
   4. A controlled tag list of 8–12 short, business-friendly tags.
2. How to set up (first 60–90 minutes)
   1. Create a Research space with these fields: Title, Date, Source, Excerpt, Summary, Tags (multi-select), Primary Tag, Why it matters.
   2. Load your tag list (example: Market Trends, Customer Insight, Competitor, Product Idea, Usability, Pricing, Regulation, Case Study).
   3. Capture one example item: paste an excerpt, add title/date/source, pick one tag—this proves the flow.
   4. Use AI to enrich: ask for a 2–3 sentence summary, up to 3 suggested tags chosen from your list, and a single-line “why it matters.” Do a quick human check before saving the AI output.
3. Daily/weekly routine (reduces decision stress)
   1. Daily (5 minutes): when you finish reading, capture 1–2 highlights into the repo. If pressed, save title+link for later enrichment.
   2. Weekly (20–30 minutes): run AI enrichment on new items, confirm primary tags, and flag duplicates.
   3. Monthly (30–60 minutes): prune tags, rename confusing tags, archive stale items.
4. What to expect
   1. Week 1: searchable items and faster retrieval for obvious queries.
   2. Month 1: discover patterns across items and fewer repeated research efforts.
   3. Ongoing: small time spent weekly keeps the system usable—no heavy engineering required.

Mini rules to keep stress low

• Limit tags to 8–12 and force one Primary Tag per item.
• Require source + date on every item—verifiability builds trust.
• Use AI for enrichment, not final decisions—always scan AI outputs before you save.

If you tell me which repo you’ll start with, I’ll give a tiny adjustment to the fields and a 2-step automation idea you can set up in under an hour.

[Jeff Bullas]

Love the daily/weekly routine — that rhythm is what makes the system stick. Let’s add one insider layer: a tiny “tag dictionary” and a couple of AI prompts that normalize tags, avoid duplicates, and link research to decisions. This keeps the repo clean as it scales.

The idea (simple, powerful): your highlights flow into one place, AI enriches them, and a second AI step normalizes tags to a controlled list, checks for duplicates, and asks for a quick human confirm. Low friction, high trust.

• What you’ll need
  • Repo (Notion/Obsidian/Google Sheet).
  • Capture tool (web clipper or PDF highlighter that exports text).
  • AI assistant (your chat tool or built-in AI).
  • Optional automation (Zapier/Make) to connect capture → repo → AI.
  • A short tag dictionary (8–12 tags + allowed synonyms).

• Fields to add (keeps quality high)
  • Title, Date, Source, Excerpt.
  • Summary (2–3 sentences).
  • Tags (multi-select) + Primary Tag.
  • Why it matters (1 sentence).
  • Evidence Type (Report, User Interview, Article, Internal Data).
  • Confidence (1–5) and Quality Notes (1 line).
  • Decision Link (which decision this supports) and Question Answered.

Tag dictionary (quick template)

• Market Trends (aliases: trend, macro, industry shift)
• Customer Insight (aliases: user need, pain point, jobs to be done)
• Competitor (aliases: rival, alt, comparison)
• Product Idea (aliases: feature, concept, roadmap)
• Usability (aliases: UX, friction, onboarding)
• Pricing (aliases: price, packaging, discount)
• Regulation (aliases: compliance, policy, legal)
• Case Study (aliases: example, success, story)

Step-by-step (90-minute setup)

1. Create the fields above in your repo and load the tag dictionary + aliases.
2. Clip one real excerpt and add Title/Date/Source to prove the flow.
3. Run the enrichment prompt (below) to generate Summary, Tags, Primary, Why it matters, Evidence Type, Confidence.
4. Run the normalizer prompt (below) to map tags to your controlled list and catch duplicates.
5. Do a 30-second human check, then save. You’ve locked in consistency.

Copy-paste AI prompt: Enrichment (use after you paste a highlight)

Role: You are a research librarian. Using the controlled tag list and aliases provided, enrich this excerpt. Return answers in exactly this format:
• Summary: [2–3 sentences]
• Tags: [up to 3 from the controlled list]
• Primary Tag: [one from the controlled list]
• Why it matters: [one sentence tied to a product/market decision]
• Evidence Type: [Report | User Interview | Article | Internal Data]
• Confidence: [1–5, where 5 = strong evidence]
• Quality Notes: [<=12 words]
Controlled tags with aliases: [paste the tag dictionary list]
Excerpt: [paste excerpt]
Source: [URL or title] | Date: [YYYY-MM-DD]

Copy-paste AI prompt: Tag Normalizer + Duplicate Check

Role: You normalize metadata for a research repository. Given the proposed fields and the controlled tag list with aliases, do two things.
1) Normalize Tags: map any suggested tags to the canonical list only. If none fit, propose exactly one new tag and mark it as Proposed.
2) Duplicate Check: compare the new item against these recent items (titles+summaries below). If any are substantially similar (>70% overlap), return their IDs.
Return answers in this format:
• Canonical Tags: [list]
• Primary Tag: [one]
• Proposed New Tag: [name or “None”]
• Possible Duplicates: [IDs or “None”]
Controlled tags with aliases: [paste dictionary]
New item: Title=[..] Summary=[..]
Recent items: [ID=1 Title=.. Summary=..] [ID=2 …] [ID=3 …]

Copy-paste AI prompt: Question-to-Answer (for retrieval)

Role: Research synthesizer. Using the provided notes (top 5 matches by search), create a concise answer with citations. Return in this format:
• Answer: [3–6 sentences]
• Top Evidence: [Title — Primary Tag — Confidence/5]
• Why it matters: [1 sentence]
• Citations: [Source links or titles]
Question: [paste]
Notes: [paste up to 5 items: Title, Summary, Primary Tag, Confidence, Source]

Worked example (quick)

1. Excerpt: “Users downgrade within 14 days due to unclear value on mid-tier plan.”
2. Enrichment returns: Summary (2 sentences), Tags=[Pricing, Customer Insight], Primary=Pricing, Why it matters=“Run value messaging test on mid-tier.” Evidence Type=User Interview, Confidence=4.
3. Normalizer maps “value messaging” to Pricing, finds a similar note ID=27. You merge them and keep the freshest summary.
4. Later, you ask: “What’s driving mid-tier churn?” The retrieval prompt surfaces both notes with citations in 10 seconds.

Mistakes to avoid (and quick fixes)

• Tag drift (too many variants). Fix: use the normalizer prompt every time and prune monthly.
• Weak summaries. Fix: enforce 2–3 sentences max and one concrete recommendation.
• No citation. Fix: make Source + Date required fields before saving.
• Duplicates hiding insights. Fix: run the duplicate check on ingest and merge immediately.
• Automation overkill. Fix: automate two steps only—create item and enrich; keep the human confirm.

1-week action plan (do-first)

1. Day 1: Set up the fields and load the 8–12 tag dictionary with aliases.
2. Day 2: Capture 5 items (Title, Date, Source, Excerpt).
3. Day 3: Run the Enrichment prompt on all 5 items; save outputs.
4. Day 4: Run the Normalizer + Duplicate Check; merge any overlaps.
5. Day 5: Add Question Answered + Decision Link to each item.
6. Day 6: Automate two steps: capture → new item, new item → Enrichment. Keep the normalizer as a manual button for now.
7. Day 7: Test 5 real questions using the retrieval prompt; note time-to-answer and adjust tags.

What to expect

• Week 1: 10–15 clean, searchable items with consistent tags.
• Month 1: Faster answers, fewer re-reads, and clear links between evidence and decisions.

Tell me your repo (Notion, Obsidian, or Sheets) and your industry. I’ll tailor the tag dictionary, add 3 high-signal tags unique to your domain, and share a 2-step automation you can set up in under an hour.

[Fiona Freelance Financier]

Nice work—this is the practical layer that keeps a research repo usable as it grows. Small routines plus an AI step that normalizes tags and checks duplicates will cut the noise without adding stress. Below is a clear, low-effort plan (what you’ll need, how to do it, and what to expect) plus a two-step automation you can set up quickly.

1. What you’ll need
   1. A single repo (Notion recommended; Obsidian or a Sheet works fine).
   2. A capture tool (browser clipper or PDF highlighter that exports text).
   3. An AI assistant you can call from your automation tool or manually in your chat app.
   4. An automation connector (Zapier, Make, or your repo’s built-in integrations).
   5. A short tag dictionary of 8–12 canonical tags with 1–3 allowed synonyms each.
2. How to set it up (step-by-step, ~90 minutes)
   1. Create fields in your repo: Title, Date, Source, Excerpt, Summary, Tags (multi-select), Primary Tag, Why it matters, Evidence Type, Confidence, Decision Link, Question Answered.
   2. Load your tag dictionary and record synonyms in a small note called Tag Dictionary.
   3. Capture one real excerpt and add Title/Date/Source to prove the flow.
   4. Enrich: ask the AI to write a 2–3 sentence summary, recommend up to three tags chosen from your dictionary, pick a single Primary Tag, name an Evidence Type, and give a 1–5 confidence score plus one-line quality note. Do a quick human check before saving.
   5. Normalize & de-duplicate: run a second check that maps any suggested tags to the canonical list (use synonyms mapping), and compares the new item against recent titles/summaries to flag likely duplicates (report any similar item IDs for merging).
   6. Save only after a 20–30 second human confirm—this keeps trust high and errors low.
3. Two-step automation (under 60 minutes)
   1. Step A — Capture → Repo: Trigger from your clipper to create a new item in the repo with Title, Date, Source, Excerpt.
      1. Action: create the item with minimal fields filled so you capture immediately.
   2. Step B — Enrich (automated, then confirm): call the AI to produce the summary, suggested tags (from your dictionary), one-line “why it matters”, evidence type, and confidence; write those back to the item as draft fields and notify you to confirm.
4. What to expect (realistic timeline)
   1. Week 1: 10–15 searchable items; basic retrieval works for obvious queries.
   2. Month 1: patterns emerge, fewer repeated reads, easier decisions tied to evidence links.
   3. Ongoing: 10–30 minutes weekly maintenance (review new items, merge duplicates, prune tags).

Mini rules to keep stress low

1. Limit canonical tags to 8–12 and enforce one Primary Tag per item.
2. Require Source + Date before final save—verifiability builds confidence.
3. Automate only capture and enrichment; keep normalization/merge as a one-click human step early on.
4. Schedule a 20–30 minute monthly tidy to rename tags and remove drift.

If you tell me which repo you’ll use (Notion, Obsidian, or Sheets) and your industry, I’ll give a short, tailored tag dictionary (with three domain-specific tags) and a two-step automation checklist you can implement in under an hour.

[Author]

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