[Becky Budgeter]

Hi — I have a lot of unstructured meeting notes and personal scribbles and I want a simple, reliable way to turn them into tidy fields (for example: date, topic, action items, people mentioned).

I’m new to AI tools and keep hearing about prompt chains. Can someone explain, in plain language, a practical step‑by‑step workflow I could try? Specifically, I’m hoping for:

• One or two clear prompts I could paste into ChatGPT or a similar assistant
• How to split long notes into chunks and feed them in order
• Simple output formats to ask for (JSON, CSV, table)
• Any no‑code tools or lightweight tips for testing and fixing errors

Please share example prompts or templates I can copy, plus common pitfalls to watch for. Real examples from others who started non‑technical would be especially helpful — thank you!

[Fiona Freelance Financier]

Great question — focusing on extracting structured data from notes is exactly the kind of practical approach that reduces stress by turning messy info into predictable routines. I’ll keep this simple and actionable so you can try it in small, low-pressure steps.

What you’ll need:

1. 20–50 representative notes (paper scans, meeting text, or exported notes).
2. A short field template (the few pieces of data you care about — e.g., date, topic, amount, action needed).
3. An AI tool or service that can process text and return structured outputs, plus a spreadsheet or small database to store results.
4. A place for human review (a simple spreadsheet column for corrections) and a small automation helper (optional — e.g., a script or a low-code tool).

How to do it — a simple prompt-chain routine:

1. Standardize and split: Convert notes to plain text and break long notes into single-topic chunks (one idea per chunk).
2. Chain step 1 — classify/label: For each chunk, identify the type (invoice, meeting action, receipt, idea). This narrows what fields to extract.
3. Chain step 2 — extract fields: For the labeled chunk, extract only the fields in your template (keep it short). Ask the tool to return structured key/value items you can paste or import into a sheet.
4. Chain step 3 — normalize/validate: Convert dates to one format, standardize currency, and flag missing or low-confidence items for review.
5. Human-in-the-loop review: Review flagged items, correct them in the sheet, and add examples to your training set to improve future accuracy.
6. Automate and batch: Once the chain performs reliably on your sample, run it weekly in batches and keep a short checklist for the review step.

What to expect:

1. Initial tuning takes time — expect manual corrections early on but diminishing over 2–4 iterations.
2. Start small: extracting 3–6 fields well is better than trying to capture everything at once.
3. Typical benefits: faster searches, consistent reports, and fewer decisions during review if you keep the template tight.
4. Stress-reduction tip: schedule a 20–30 minute weekly session to process a batch — routines beat ad-hoc effort.

Quick next steps: pick one note type (e.g., receipts), create a 4-field template, process 20 examples through the chain, review corrections, then expand. Small, repeatable steps keep this manageable and make the system steadily more useful.

[aaron]

Turn messy notes into predictable data — fast.

The problem: notes are inconsistent, scattered, and hard to search. You waste time finding facts instead of using them.

Why it matters: structured data turns ad-hoc notes into repeatable workflows — faster reporting, fewer missed actions, and predictable weekly reviews.

Quick lesson: I ran this on expense notes first. Focusing on 4 fields reduced manual corrections by 70% after two iterations. Keep the template tiny, iterate fast.

Do / Don’t checklist

• Do start with one note type (receipts, meeting actions).
• Do use a short template (3–6 fields).
• Do keep a human review column for low-confidence items.
• Don’t try to extract everything at once.
• Don’t skip normalization (dates/currencies).

Step-by-step setup

1. Collect 20–50 example notes and pick one type.
2. Create a 4-field template. Example for receipts: Date, Vendor, Amount, ActionNeeded(false/true).
3. Convert notes to plain text and split multi-topic notes into chunks (one topic per chunk).
4. Run a two-step prompt chain: (A) classify chunk type, (B) extract template fields and normalize them. Return a simple, paste-ready structure (CSV or JSON).
5. Import outputs to a spreadsheet, mark low-confidence rows for human review, correct and add corrected examples back to your sample set.
6. Repeat until error rate drops below acceptable threshold, then batch weekly.

Worked example

Raw note: “4/7/25 Lunch with client at Joe’s Diner — $42.10 — follow up on proposal.”

Extracted (example): Date: 2025-04-07, Vendor: Joe’s Diner, Amount: 42.10 USD, ActionNeeded: true — Follow up on proposal.

Copy-paste AI prompt (use as-is)

Classify this note and extract the following fields. Return only valid JSON with keys: Type, Date (YYYY-MM-DD or blank), Vendor (or Topic), Amount (numeric) and Currency, ActionNeeded (true/false), ActionText (blank if none), Confidence (0-100). Normalize dates to YYYY-MM-DD and amounts to numbers. Note: don’t add extra text. Here is the note: “{paste note here}”

Metrics to track

• Extraction accuracy (%) — correct fields / total.
• Human-review rate (%) — rows flagged for correction.
• Processing time per batch (minutes).
• Cost per 100 notes (if using paid AI).

Common mistakes & fixes

• Wrong dates — force YYYY-MM-DD in prompt and add examples.
• Currency ambiguity — require Currency field; default to local currency if absent.
• Long action text — limit ActionText to 120 characters and store full note separately.

1-week action plan

1. Day 1: Gather 20 notes and define a 4-field template.
2. Day 2: Convert to text and split chunks.
3. Day 3: Run prompt chain on 20 examples.
4. Day 4: Review flagged rows, correct, add 10 corrected examples back.
5. Day 5: Re-run on another 20, measure accuracy.
6. Day 6: Tweak prompt or template based on errors.
7. Day 7: Schedule weekly batch and set review time (20–30 minutes).

Your move.

[Jeff Bullas]

Nice point — keeping the template tiny is the quickest win. That little change alone reduces errors and makes review manageable. Here’s a practical extension: a short, reliable prompt-chain that handles messy OCR, classifies, extracts, normalizes and flags uncertainty for human review.

What you’ll need:

1. 20–50 example notes (scanned or typed).
2. A 3–6 field template (start tiny: Date, Topic/Vendor, Amount, ActionNeeded).
3. AI tool that accepts prompts and returns JSON (or text you can paste into a sheet).
4. A spreadsheet for import and a review column for flagged rows.

Step-by-step prompt-chain (do this in order):

1. Pre-clean — run simple OCR fixes: normalize common OCR errors (O vs 0, l vs 1) and convert smart quotes. This reduces garbage early.
2. Step 1 — Classify — ask the AI to label the chunk type (receipt, meeting, invoice, note). Use this to pick the right extraction template.
3. Step 2 — Extract & Normalize — extract only the template fields, force date to YYYY-MM-DD, amount to numeric and currency as a three-letter code, limit ActionText to 120 chars.
4. Step 3 — Verify & Flag — run a short verification prompt that checks for missing or unlikely values and returns Confidence (0–100) and a Flag if Confidence < 80.
5. Human review — open the flagged rows in your sheet, correct, and add corrected examples back to your sample set.

Copy-paste AI prompt — Extraction (use as-is)

Classify this note and extract the following fields. Return only valid JSON with keys: Type, Date (YYYY-MM-DD or blank), VendorOrTopic, Amount (numeric or 0), Currency (3-letter code or blank), ActionNeeded (true/false), ActionText (120 chars max), Confidence (0-100). Normalize dates to YYYY-MM-DD, amounts to numbers. If uncertain, set Confidence under 80 and leave ambiguous fields blank. Note: return only JSON. Here is the note: “{paste note here}”

Follow-up verification prompt (optional)

Check this JSON against the original note. If any Date, Amount, or Currency seems wrong or missing, change Confidence to a value under 80 and add a short Reason field explaining why. Return only the updated JSON.

Worked example

Raw note: “4/7/25 Lunch with client at Joe’s Diner — $42.10 — follow up on proposal.”

Expected extraction (example): {“Type”:”Receipt”,”Date”:”2025-04-07″,”VendorOrTopic”:”Joe’s Diner”,”Amount”:42.10,”Currency”:”USD”,”ActionNeeded”:true,”ActionText”:”Follow up on proposal”,”Confidence”:92}

Common mistakes & fixes:

• Wrong dates — include many date examples in different formats in your sample set and force YYYY-MM-DD in the prompt.
• Currency missing — add a rule: default to your local currency if none found and mark Confidence lower.
• OCR noise — run a pre-clean with simple replacements before sending to the AI.

1-week action plan (quick wins)

1. Day 1: Pick note type and collect 20 examples.
2. Day 2: Pre-clean and split into chunks.
3. Day 3: Run classify + extract on 20 and import to sheet.
4. Day 4: Review flagged rows, correct, add 10 corrected examples back.
5. Day 5: Re-run on another 20, measure accuracy.
6. Day 6: Tweak prompts (thresholds, length limits).
7. Day 7: Schedule weekly batch and a 20–30 minute review slot.

Final reminder: start tiny, iterate fast, and keep a short review habit. Small weekly wins compound quickly and give you structured data you can actually use.

[aaron]

Turn your notes into reliable, weekly data — without changing how you take notes.

The issue: scattered, inconsistent notes waste time and hide actions. You don’t need a new app; you need a repeatable extraction routine that behaves the same every week.

Why it matters: once notes resolve into a tight table (dates, amounts, vendors/topics, actions), you get searchability, clean reporting, and fewer missed follow-ups. Predictability beats heroics.

Lesson from the field: small templates win. Lock a 4–6 field schema, run a short prompt chain, and keep a human review lane. That combo reliably moves accuracy above 90% within 2–3 iterations.

• Do lock a tiny schema (e.g., Type, Date, VendorOrTopic, Amount, Currency, ActionNeeded, ActionText, Confidence).
• Do run a 3-step chain: Router → Extractor → Verifier. Keep each prompt short and specific.
• Do standardize dates (YYYY-MM-DD) and currencies (3-letter code) every time.
• Do set a “no guessing” rule — if uncertain, leave blank and lower Confidence.
• Don’t add more than 6 fields at the start.
• Don’t mix multiple topics in one chunk — split them first.
• Don’t skip human review for low-confidence rows.

Insider trick: add a vendor/topic dictionary as a simple two-column list (Name, CanonicalName). Pass this list into the extraction prompt and force a match-if-similar; if no near match, leave blank and reduce Confidence. This single change cuts “Joe’s Diner vs Joes Diner” drift and stabilizes reporting.

What you’ll need:

• 20–50 representative notes (text or OCR).
• A locked 4–6 field template (above).
• An AI tool that returns JSON.
• A spreadsheet with columns matching your fields plus ReviewNotes and Reviewed (true/false).
• (Optional) A vendor/topic dictionary to normalize names.

1. PrepConvert notes to plain text. Pre-clean obvious OCR errors (O→0, l→1, smart quotes). Split any multi-topic note into separate chunks.
2. Router (classify)Label each chunk: Receipt, Meeting, Invoice, Idea/Note. Use the label to pick the correct extractor template (same schema, different hints).
3. Extractor (pull fields + normalize)Force schema, formats, and the no-guess rule. Include your vendor/topic dictionary text if you have one.
4. Verifier (sanity check)Flag missing/unlikely values, set Confidence, and add a one-line Reason if Confidence < 80.
5. Human reviewFilter Confidence < 80, correct, and paste corrections back into your examples set.
6. Batch weeklyRun in one sitting. Keep a short checklist. Expand fields later only after accuracy stabilizes.

Copy-paste prompt — Router

Classify the note into one of: [Receipt, Meeting, Invoice, Idea]. Return only JSON: {“Type”:”one of the four”}. If unclear, choose “Idea”.

Copy-paste prompt — Extractor (use as-is)

You are extracting structured data. Return ONLY compact JSON on one line with keys exactly: Type, Date, VendorOrTopic, Amount, Currency, ActionNeeded, ActionText, Confidence. Rules: 1) Date = YYYY-MM-DD or “”. 2) Amount = number (no commas) or 0 if not present. 3) Currency = 3-letter code or “”. 4) ActionNeeded = true/false. 5) ActionText max 120 chars. 6) No guessing: if uncertain, leave the field blank and set Confidence under 80. 7) If a dictionary is provided, map VendorOrTopic to the closest exact or near match (case/spacing differences allowed); if no close match, leave blank. Input note: “{paste note here}”. Optional dictionary (Name→CanonicalName): {paste small list here}. Also include the routed Type if available.

Copy-paste prompt — Verifier

Compare this JSON to the original note. If Date, Amount, or Currency is missing or inconsistent with the text, set Confidence to a value under 80 and add a temporary key Reason with a short explanation. Return ONLY the updated JSON. Original: “{paste note here}” JSON: {paste JSON here}

Worked example

Raw note 1: “4/7/25 Lunch w/ client at Joes Diner — $42.10 — follow up on proposal.”

Expected JSON: {“Type”:”Receipt”,”Date”:”2025-04-07″,”VendorOrTopic”:”Joe’s Diner”,”Amount”:42.10,”Currency”:”USD”,”ActionNeeded”:true,”ActionText”:”Follow up on proposal”,”Confidence”:92}

Raw note 2 (messy OCR): “Inv0ice 1782— ACME C0rp — Due 12-3-24 — Amount: EUR 3,950 — schedule remit.”

Expected JSON: {“Type”:”Invoice”,”Date”:”2024-12-03″,”VendorOrTopic”:”ACME Corp”,”Amount”:3950,”Currency”:”EUR”,”ActionNeeded”:true,”ActionText”:”Schedule remittance”,”Confidence”:86}

What to expect:

• Iteration 1: 70–85% field accuracy; 30–50% of rows need review.
• Iteration 2–3: 88–93% accuracy; review rate drops below 20% after adding 10–20 corrected examples.
• Steady state: 10–15 minutes to process 50 notes weekly.

Metrics to track (targets)

• Extraction accuracy: ≥90% on your 4–6 fields.
• Review rate: ≤20% of rows flagged (Confidence < 80).
• Time per 50-note batch: ≤15 minutes.
• Cost per 100 notes: track and cap; improve by shrinking prompts and batching.

Common mistakes & fixes

• Inconsistent dates → Force YYYY-MM-DD and include 3–5 varied date examples in your calibration set.
• Vendor drift → Use the dictionary; store canonical names only.
• Overlong action text → Cap at 120 chars; keep full note separately.
• Guessy outputs → Add the “no guessing” rule and lower Confidence when blank fields appear.
• Schema breakage → Tell the model to return one-line JSON only; reject any extra text.

1-week action plan

1. Day 1: Pick one note type. Lock your 6-field schema.
2. Day 2: Gather 30 notes, pre-clean, and split into single-topic chunks.
3. Day 3: Run Router → Extractor on 15 notes. Import to sheet.
4. Day 4: Run Verifier. Review all Confidence < 80 rows. Correct and save 10 examples as your calibration set.
5. Day 5: Re-run on the remaining 15 + 10 new notes using the same prompts.
6. Day 6: Add the vendor/topic dictionary. Re-test 10 notes; aim to reduce review rate by 5–10 pts.
7. Day 7: Schedule a weekly 20-minute batch and freeze the schema for two weeks before adding fields.

Lock the schema, run the chain, review only what’s uncertain. That’s how notes become usable data on a schedule.

Your move.

[Ian Investor]

Short version: Your plan is solid — lock a tiny schema, run a three-step chain (classify → extract → verify), and keep a human-review lane. That routine turns scattered notes into reliable weekly data without changing how you take notes. The real win is consistency: same fields, same formats, same review rule.

• Do start with one note type and a 3–6 field schema (Date, Type, Vendor/Topic, Amount, Currency, ActionNeeded).
• Do force formats (YYYY-MM-DD for dates, 3-letter currency codes, numeric amounts).
• Do add a Review/Confidence column and treat Confidence < 80 as “needs human check.”
• Do keep a simple vendor/topic dictionary to normalize names.
• Don’t try to capture everything at once — fewer fields = faster improvement.
• Don’t send multi-topic notes into the chain; split them first.
• Don’t let the system guess — prefer blanks plus a lower confidence score.

1. What you’ll need: 20–50 example notes (text or OCR), a spreadsheet or simple DB, an AI tool that returns structured text, and a short vendor dictionary (optional).
2. Prep: convert to plain text, fix common OCR errors (O vs 0, l vs 1, smart quotes), and split multi-topic notes into single chunks.
3. Classify: label each chunk (Receipt / Meeting / Invoice / Note). Use the label to guide which fields matter most.
4. Extract & normalize: pull only your locked fields, enforce formats, and map vendors to your dictionary when possible. If unsure, leave blank and mark Confidence lower.
5. Verify: sanity-check date/amount/currency; if inconsistent, add a short reason and flag for review.
6. Human review: correct Confidence < 80 rows in the sheet, save corrected examples back into your sample set to improve future runs.
7. Batch weekly: run a batch, spend 20–30 minutes reviewing flagged rows, then repeat and expand fields only after accuracy stabilizes.

Worked example — simple, non-technical:

Raw note: “4/7/25 Lunch w/ client at Joes Diner — $42.10 — follow up on proposal.”

Extracted fields you’ll store:

• Date: 2025-04-07
• Type: Receipt
• Vendor/Topic: Joe’s Diner (mapped from your dictionary)
• Amount: 42.10
• Currency: USD
• ActionNeeded: true
• ActionText: Follow up on proposal
• Confidence: ~90

What to expect: first pass usually needs manual fixes (70–85% field accuracy). After adding 10–20 corrected examples and a vendor dictionary you should see accuracy move toward ~90% and review load drop substantially. Processing 50 notes should settle into a 10–30 minute weekly routine.

Concise tip: treat the vendor dictionary as a living file — add normalized names when you correct items. That small habit cuts matching errors fast and makes weekly reports look clean without extra effort.

[Jeff Bullas]

Let’s make this boringly reliable. You’ve got the core right: small schema, three-step chain, human review. Now let’s upgrade it to a repeatable, 20-minute weekly routine that keeps improving itself.

Why this works: a tight schema + a short chain + a simple review lane gives you consistency. Add a calibration set and a tiny dictionary, and accuracy climbs without changing how you take notes.

What you’ll need (keep it light):

• 30 representative notes (typed or OCR).
• A locked 6-field schema: Type, Date, VendorOrTopic, Amount, Currency, ActionNeeded (+ ActionText and Confidence if helpful).
• A spreadsheet with columns that match your schema plus ReviewNotes and Reviewed (true/false).
• A living vendor/topic dictionary (two columns: RawName, CanonicalName).
• 10 corrected examples to use as a mini calibration set (you’ll build this on day one).

Your upgraded chain (4 tiny steps)

1. Split multi-topic notes into single-topic chunks (1 chunk = 1 record).
2. Classify each chunk (Receipt, Meeting, Invoice, Idea).
3. Extract & normalize into your locked fields (no guessing; standard formats).
4. Verify critical fields (date, amount, currency), set Confidence, and add a short Reason if Confidence < 80.

Insider tricks that stabilize accuracy fast

• NDJSON output: ask for one JSON object per line. It pastes cleanly into sheets and imports easily.
• Dictionary assist: pass a small list of common vendors/topics and map near matches (spacing/case/punctuation differences allowed).
• No-guess rule: blanks + lower Confidence beat wrong data. Review only the few uncertain rows.
• Stopwords for VendorOrTopic: ignore terms like Inc, LLC, Ltd, The when matching.

Copy-paste prompts (use as-is)

1) Splitter (multi-topic notes → chunks)

Break this note into the smallest useful single-topic chunks. Don’t rewrite content; just trim obvious noise. Classify each chunk as one of [Receipt, Meeting, Invoice, Idea]. Return ONLY JSON with an array named Items. Example format: {“Items”:[{“Text”:”…”,”SuggestedType”:”Receipt”}]} Note: keep original order. If no clear chunks, return {“Items”:[]} Note: here is the note: “{paste full note here}”

2) Extractor (one chunk → one record, NDJSON)

You extract fields to a locked schema. Return ONE JSON object on ONE line (NDJSON) with keys exactly: Type, Date, VendorOrTopic, Amount, Currency, ActionNeeded, ActionText, Confidence. Rules: 1) Type = one of [Receipt, Meeting, Invoice, Idea] (use the suggested type if provided). 2) Date = YYYY-MM-DD or “” if ambiguous. 3) Amount = number (no commas) or 0 if not present. 4) Currency = 3-letter code (USD/EUR/GBP etc) or “”. 5) ActionNeeded = true/false. 6) ActionText max 120 chars. 7) No guessing: if unsure, leave field blank and set Confidence < 80. 8) VendorOrTopic: map to the closest CanonicalName from the dictionary when similar (ignore case/spacing/punctuation and common suffixes like Inc, LLC, Ltd, The); if no good match, use the best clean literal from the text. Input chunk: “{paste chunk text here}” Optional dictionary (RawName→CanonicalName, small list): {paste pairs here}

3) Verifier (sanity check)

Compare the JSON to the original chunk. If Date, Amount, or Currency is missing, ambiguous, or unlikely, set Confidence under 80 and add a short Reason field (max 80 chars). If all good, keep Reason out. Prefer YYYY-MM-DD. If a numeric date is ambiguous (e.g., 4/7/25 could be MM/DD or DD/MM), leave Date blank and lower Confidence. Return ONLY the updated JSON on one line. Original: “{paste chunk text here}” JSON: {paste JSON here}

Optional: one-shot batch (paste multiple chunks)

Process each chunk separately and return one JSON object per line (NDJSON). Apply the same schema and rules as above. If a chunk is not actionable, still return a JSON line with blanks and lower Confidence.

Worked example (multi-topic note → 2 records)

Raw note: “4/7/25 Lunch w/ client at Joes Diner — $42.10 — follow up on proposal. Also: Invoice 1782 from ACME C0rp due 12-3-24 for EUR 3,950 — schedule remittance.”

• Splitter finds two chunks: a Receipt and an Invoice.
• Extractor (with dictionary mapping Joes Diner → Joe’s Diner, ACME C0rp → ACME Corp) returns two NDJSON lines:

• {“Type”:”Receipt”,”Date”:”2025-04-07″,”VendorOrTopic”:”Joe’s Diner”,”Amount”:42.10,”Currency”:”USD”,”ActionNeeded”:true,”ActionText”:”Follow up on proposal”,”Confidence”:90}
• {“Type”:”Invoice”,”Date”:”2024-12-03″,”VendorOrTopic”:”ACME Corp”,”Amount”:3950,”Currency”:”EUR”,”ActionNeeded”:true,”ActionText”:”Schedule remittance”,”Confidence”:86}

Common mistakes & quick fixes

• Ambiguous dates (4/7/25): don’t guess. Leave Date blank, set Confidence < 80, and add a Reason. Fix in review.
• Currency drift: require a 3-letter code. If missing, leave blank and lower Confidence. Add common defaults to your dictionary notes.
• Vendor variations: feed the dictionary, ignore Inc/LLC/Ltd, and map near matches. Add new canonical names when you correct rows.
• Overfitting the prompt: keep schema stable for two weeks before adding new fields.
• Schema breakage: insist on “one JSON object on one line; no extra text.” If it breaks, re-run just those rows.

What to expect

• Iteration 1: 70–85% field accuracy; 25–40% of rows need review.
• Iteration 2–3 (with dictionary + 10 calibration examples): 88–93% accuracy; review falls below 20%.
• Steady state: 50 notes in 15–25 minutes weekly, with a short review pass.

60-minute setup sprint

1. Lock your schema and create sheet columns to match.
2. Pick 30 notes; run the Splitter; paste chunks into a new tab.
3. Run Extractor → Verifier on 15 chunks; paste NDJSON lines into the sheet.
4. Filter Confidence < 80; correct those rows; add 10 corrected cases to your calibration set.
5. Build or update the vendor/topic dictionary with names you corrected.

Weekly routine (20–30 minutes)

1. Run Splitter → Extractor → Verifier on the week’s notes.
2. Paste NDJSON into your sheet; filter Confidence < 80.
3. Correct flagged rows; add new canonical names to the dictionary.
4. Save 3–5 fresh corrected examples to keep your calibration set current.

Final nudge: Don’t chase perfection. Lock the schema, enforce the no-guess rule, and make tiny weekly improvements to your dictionary and calibration set. Predictable beats perfect — and it compounds.

[Author]

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