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A practical guide and starter tool for extracting decisions, owners, deadlines, risks, unresolved questions, and source snippets from notes or transcripts before routing them into a lightweight action and follow-up queue.
Paste synthetic notes or a sanitized transcript shape. The starter tool extracts local queue items and keeps source snippets attached for review.
Use synthetic examples or sanitized placeholders. Do not paste private workplace notes, client data, or company-private transcripts.
Route extracted decisions, actions, risks, and questions into review before sending follow-through.
choose the spreadsheet-first pilot because the team can run it on normal office laptops.
Source snippet: Decision: choose the spreadsheet-first pilot because the team can run it on normal office laptops.
Maya drafts the follow-through note by Wednesday at 3pm.
Source snippet: Action: Maya drafts the follow-through note by Wednesday at 3pm.
Sam will update the decision log by Friday.
Source snippet: Sam will update the decision log by Friday.
transcript quality is uneven, so source snippets must stay attached for review.
Source snippet: Risk: transcript quality is uneven, so source snippets must stay attached for review.
who owns the escalation path if the data export slips?
Source snippet: Open question: who owns the escalation path if the data export slips?
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Use synthetic examples or public-source notes only. Do not paste private workplace notes, client data, or company-private transcripts.
Raw notes are hard to scan after the meeting ends. The useful object is an inbox that separates decisions, actions, risks, open questions, source snippets, and next checks.
Start with a deterministic extraction pass and a small review queue. Once the queue earns trust, add batch processing, SQLite or DuckDB storage, retrieval, and LLM labeling for ambiguous snippets.
Use a spreadsheet, SQLite table, or DuckDB-backed hot mart. The schema is intentionally small so it works on normal office laptops and can grow into a local dashboard later.
The system should feel boring: ingest, normalize, extract, materialize, review, and retrieve. That is what keeps it useful on everyday machines instead of turning every meeting into a live AI event.
Collect meeting text without making the raw transcript the working surface.
Split text into timestamped or line-based snippets so extraction output can cite source snippets.
Find decisions, owners, deadlines, risks, unresolved questions, and follow-up commitments.
Route extracted items into a follow-up queue that is fast to scan on normal office laptops.
Keep humans responsible for final decisions, deadlines, and messages.
Make repeated decisions and unresolved questions easier to find before the next meeting.
Keep the first version simple: Markdown input, deterministic extraction, SQLite queue tables, and a weekly DuckDB read model if you want metrics. Add embeddings, BM25, and RRF only after you have enough reviewed source snippets to make retrieval worthwhile.
Ingest notes or transcripts into a local working folder before extracting action items.
Split the source into stable source snippets so every item can be reviewed.
Extract decisions, owners, deadlines, risks, unresolved questions, and follow-up queue items.
Keep the decision log separate from raw notes so decisions remain easy to retrieve.
Store the starter inbox in SQLite, DuckDB, or a spreadsheet that runs on normal office laptops.
Use BM25, embeddings, and RRF only after the basic follow-up queue is useful.
Add an llm labeling queue for ambiguous snippets instead of asking a model to rewrite every meeting live.
Do not paste private workplace notes, client data, or company-private transcripts into public tools.
Review source snippets before sending any follow-through, escalation, or alignment message.
Practice with generalized examples first. The value comes from the extraction shape, not from publishing sensitive workplace detail.
The example includes decisions, owners, deadlines, risks, unresolved questions, source snippets, and a follow-up queue path without relying on private workplace material.
Decision: choose the spreadsheet-first pilot because the team can run it on normal office laptops. Action: Maya drafts the follow-through note by Wednesday at 3pm. Risk: transcript quality is uneven, so source snippets must stay attached for review. Open question: who owns the escalation path if the data export slips? Sam will update the decision log by Friday.
The example shows how action extraction can support lightweight dashboards, decision memory, and review gates.
Decision: keep the dashboard as a static snapshot for the first release. Action: Priya confirms the owner list before Thursday noon. Risk: live refresh adds support burden before the workflow is validated. Question: what signal moves this from weekly review to daily review? Alex will route open items into the follow-up queue on Friday.
The example connects meeting follow-through with compute-aware local tooling and human confirmation.
Decision: use the existing SQLite table for queue state and add DuckDB only for batch reporting. Action: Jordan writes the review checklist by Tuesday. Risk: if owner fields are missing, follow-up messages will be vague. Open question: which snippets need an llm labeling queue before the next review?
AI can help once your source handling is safe. Use prompts to label, verify, and route snippets, not to replace human confirmation.
Convert synthetic meeting notes into structured queue items.
Do not paste private notes, confidential workplace details, client data, or company-private transcripts into the model.
Check whether each extracted item has inspectable source evidence.
Keep source snippets short and sanitized. Human review owns the final action and follow-up queue.
Route extracted items into the next follow-up state.
Use public-source placeholders and synthetic examples only; do not publish claims about a real workplace.
Connect current meeting output to prior decision log entries.
Use AI to draft review notes, not to decide accountability or escalation without human confirmation.
Meeting extraction works best when it feeds a stakeholder update system, a workplace communication review pass, and materialized retrieval outputs for prior decisions.
It is a lightweight workflow that turns meeting notes into decisions, actions, risks, unresolved questions, source snippets, and a follow-up queue for human review.
No. Use synthetic, sanitized, or public-source examples. Do not paste private workplace notes, confidential transcripts, client data, or company-private details.
No. The on-page starter tool uses deterministic local extraction rules. The guide includes AI prompt patterns for private workflows once you have safe data handling.
Add retrieval after the basic queue is useful. Start with a small SQLite or DuckDB record, then materialize retrieval outputs for prior decisions and source snippets.
Pick one meeting, extract the queue manually once, verify source snippets, and only automate the parts that keep showing up every week.
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