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Build a Self-Improving AI Meeting Protocol for Teams This workflow was inspired by a question Brooke Benson posted in the Rundown Hub about meeting transcripts that miss action items or assign them to the wrong person. That felt familiar because I used to take meeting minutes as a management assistant. I often had to ask: Who said this? Was it a decision or only a suggestion? Who owns this action? Was the deadline confirmed? As a human note-taker, I could ask the people in the room and summarize while listening. A speaker might say: “Maybe Friday, but that depends on Marc.” My notes might become: “Friday is tentative and depends on Marc.” A transcriber captures more, but it also captures the verbal spaghetti. So the problem is not only transcription quality. It is ambiguity. Instead of only asking AI to interpret a messy transcript afterwards, what if the meeting itself produced clearer signals? The system has four layers: Step-by-step: 1. Meeting grammar Speaking habits that clarify speakers, decisions, actions, ownership and uncertainty. 2. Team information Context about names, roles, responsibilities, terminology and decision scope. 3. Output template A fixed structure for the meeting report. 4. Feedback loop AI compares corrections with its previous output and suggests improvements. The meeting grammar should stay lightweight. At the start, each participant states their name and role. Name the next speaker when possible: “Marc, go ahead.” “Sofie, what do you think?” Unclear contributions can be restated: “So Marc, Friday is possible only if the technical review is complete. Correct?” Mark proposals, decisions and actions differently: Proposal: “I suggest we move the launch to Friday.” Decision: “We are moving the launch to Friday.” Action: “Marc will update the schedule by Wednesday.” Tentative action: “Marc may update it, pending confirmation.” At transition points, repeat what was agreed: “The decision is Friday.” “Marc owns the schedule update.” “The deadline is Wednesday.” Uncertainty should also be explicit: “Owner to confirm.” “Deadline is provisional.” “This is a proposal, not a decision.” The AI should receive these rules directly: - Treat explicit decision language as confirmed. - Do not assign an owner unless ownership is clear. - Label tentative deadlines as tentative. - Flag uncertain speakers instead of guessing. - Prefer later explicit summaries over earlier ambiguous wording. The AI should also receive team context: name, role, responsibilities, decision authority, terminology and communication patterns. Example: Pieter Vermeulen Operations Usually responsible for scheduling and logistics. Often uses tentative language for proposals. Only assign ownership when he explicitly confirms it. These notes should stay transparent and editable. People should be able to review, change or reject notes about their own communication patterns. The report should use a fixed structure: Meeting title Date Participants Main topics Decisions Action items Owner Deadline Status Open questions Risks or dependencies Items requiring confirmation TL;DR This can be built into a shared internal assistant. It receives the meeting grammar, team information, transcript and output template. It should be conservative about certainty: Owner unclear → “Owner to confirm” Suggested deadline → “Tentative deadline” Unknown speaker → “Speaker unclear” Discussed proposal → “Proposal discussed, not confirmed” A human still reviews speaker attribution, decisions, owners, deadlines and confirmed items. Then the corrected report is fed back to the AI: “Which errors came from unclear meeting signals? Which came from missing team context? Which came from your interpretation? What should be improved?” The AI can suggest updates to the meeting grammar or team information. Example: Error: Several people discussed a task, but nobody clearly accepted ownership. Suggested grammar update: “When assigning an action, confirm owner and deadline in one sentence.” Or: Observed pattern: “Pieter often says ‘we should’ when proposing something.” Suggested profile update: “Treat ‘we should’ as a proposal unless explicitly confirmed.” Profile changes should be reviewed before they are stored. The workflow: meeting → transcript → AI report → human correction → error analysis → grammar/team-context update → review → next meeting Over time, fewer errors should need correction because the system becomes easier to interpret. The division of labor is simple: The transcriber captures everything. Humans resolve meaning and ambiguity. AI structures, summarizes and detects patterns. AI output quality depends not only on the prompt, but on the signals the AI receives. Sometimes the best improvement is to reduce ambiguity before it reaches the model.

I submit two reports to my directors each month. Both use structured templates, but the updates come from multiple people and sources. Some information is unstructured, such as hallway conversations that are not always captured in email, so I have struggled to get the accuracy I need when using AI to build the reports. I am currently testing CoWork and Copilot Work IQ. These reports used to take me hours, so I am already saving time, but I want to refine the process further. I believe a better way to save and store the source data could improve the final outputs, although I have not worked out the right approach yet. Step-by-step: 1. I collect updates from multiple people and sources for the two monthly reports. 2. I organize the information into the structured templates required by my directors. 3. I use CoWork and Copilot Work IQ to help build the reports. 4. I account for unstructured updates, including information from hallway conversations that may not have been captured in email. 5. I am exploring better ways to save and store the source data so I can improve the accuracy of the reports while continuing to save time.

I built a workflow to evaluate whether communication with a legal counseling service was as slow and incomplete as it felt. Email was my primary—and necessary—communication channel with them. I had become increasingly dissatisfied with delayed replies, partial answers, and questions that seemed to remain unresolved. Rather than relying only on memory or frustration, I asked GPT to review the relevant Gmail correspondence and turn it into a structured communication inventory. The workflow identified my outgoing questions, their replies, and whether each question had been fully answered, partially answered, or left open. From that inventory, we could calculate concrete indicators such as the median response time, the longest delay between a question and a substantive reply, and the number of questions that remained unresolved or were only partly addressed. This was useful because long email threads can create a distorted sense of what happened. A few frustrating exchanges can dominate memory, while other delays or omissions disappear into dozens of messages. Structuring the correspondence made the pattern measurable. The analysis was not meant to decide whether the counselors were “good” or “bad.” It was meant to answer narrower questions: How quickly were questions usually answered? Which ones were not answered? Were replies resolving the issues raised, or only responding to part of them? Afterward, we created a clear list of the questions that were still open. I used that list as a set of dossier questions when moving the case to another counselor, turning the analysis into practical continuity rather than just a complaint about the past. In simple terms: Gmail correspondence → question-and-response inventory → response-time and completeness analysis → unresolved-question list → handover to another counselor What I liked about this workflow is that it turned a vague feeling that “this communication is not working” into a documented overview I could actually use. Step-by-step: 1. I gathered the relevant Gmail correspondence with the legal counseling service. 2. I asked GPT to identify my outgoing questions, the counselors’ replies, and the status of each question. 3. I classified each question as fully answered, partially answered, or left open. 4. I calculated indicators including the median response time, the longest delay before a substantive reply, and the number of unresolved or partly addressed questions. 5. I created a clear list of the questions that remained open. 6. I used that list as dossier questions when handing the case over to another counselor.

When medical care, psychosocial support, and administrative follow-up are spread across services, emails, documents, and people over many years, there can be plenty of information but very little overview. A new provider sees snapshots, old decisions hide in email threads, and referrals resurface months later. Reconstructing the full story from memory every time requires almost a second brain. So I built a living chronological timeline. I did not want a summary of what my situation was “more or less.” I wanted: `date → event → action taken` For example: `14 March 2026 — Symptoms discussed with service X — Service refers me to Y; appointment requested.` This shows not only what happened, but what followed. My timeline runs from 2009 to the present and is divided into five documents covering meaningful periods. A new document begins when a new phase in life or support begins. Older periods stay stable, while almost all active maintenance happens in the latest document. I reopen historical files mainly when a missing record resurfaces. Step-by-step: 1. I collect existing traces, including emails, appointments, referrals, medical or administrative documents, formal decisions, and earlier notes. A sent email is often only half an event, so I also search for replies and follow-up. 2. I search by trajectory rather than by one name. I use names, spelling variants, organisations, domains, senders, recipients, subject words, and attachment terms. 3. I read the full conversation. A search result is not yet a timeline item, so I establish what happened, when it happened, who was involved, and what action or response followed. 4. I separate evidence levels. I distinguish between my own report at the time, a response or confirmation, an official decision or document, and an event that seems real but still lacks confirming evidence. I do not replace a missing document with an assumption; the absence itself becomes part of the record. 5. I track people and organisations separately. “Contact with organisation X” can hide that different people suggested, revisited, or formally communicated different things. 6. Before adding a new event, I compare it with the existing timeline. It may add information by filling a gap, replace an incomplete entry, clarify an existing event, or mark a missing record while preserving uncertainty. This prevents duplicate versions of the same event. 7. For substantial historical work, I first create a temporary review document using `date — event — action taken`. It can contain open issues, missing documents, and uncertainties. Only after review do I update the main timeline. For ordinary new events, I skip this step when the source and date are clear. 8. I make uncertainty visible by grouping research notes into “Clearly found,” “Possible identity confusion,” and “Not found or insufficiently documented.” 9. I maintain the living timeline using the structure `year → month → chronological events`. Older information is preserved, duplicates are merged, uncertainties stay visible, and open trajectories remain open. In practice, the five documents behave like version control: the first four are mostly archive, while the fifth is alive. 10. I reuse the timeline for thematic analysis. Chronology is the factual foundation, but events can also be regrouped to reveal patterns in functioning, work capacity, transport, medical developments, administrative burden, communication needs, and realistic conditions for participation. Two workflows emerged: - Historical reconstruction: collect sources → reconstruct trajectory → determine evidence status → review → update the correct historical period - Ongoing follow-up: new event → verify source → record date and action → add to the active document Most records are organised around institutions. A GP sees one part, a psychologist another, and an agency only its own procedure. Emails are chronological within a mailbox, but not across a life. A personal timeline turns that model around. The person becomes the organising axis, not the institution. That lets administratively separate events appear next to each other in time, revealing patterns such as repeated referrals, waiting times, administrative burden, or support disappearing when the need for it increases. The workflow in one line is: `Collect sources → reconstruct trajectories → connect events to actions → distinguish evidence status → compare → review → integrate → maintain → analyse thematically.` The result is not a perfect record, but a steadily improving map of what actually happened, with mainly the newest edge continuing to grow while the past stays calmly in place.

I use AI to turn my watch history into a personal recommendation engine that can answer situational questions such as, “What should I watch tonight?” Generic recommendation algorithms know what is popular and what resembles something I clicked before. They usually know much less about why I want to watch something tonight. For this workflow, I give AI two different datasets: - Already watched: Evidence of my actual viewing history and taste. - Want to watch: Shows my curiosity, intentions, and unexplored directions. This should not automatically be treated as proof that I will like something. AI analyzes both lists for patterns such as genre, themes, emotional intensity, pacing, humor, visual atmosphere, storytelling style, cultural interests, darkness versus comfort, realism versus imagination, and other recurring preferences. Instead of reducing everything to genres, the workflow builds a descriptive taste profile. It keeps confirmed preferences separate from hypotheses based on the watchlist and uses my later reactions to refine the profile. When I want a recommendation, I add my current context: available time, mood, energy, desired emotional intensity, whether I want something comforting or challenging, and whether I want a movie or an episode. The system matches that temporary context against my longer-term taste profile and the available watchlist. So instead of asking: > “Recommend me a good series.” I can ask: > “I have about 90 minutes, my brain is tired, I want something comforting but not stupid, and I don't want anything emotionally brutal tonight.” The recommendation is based on three layers at once: past taste, future curiosity, and present state. Over time, the system becomes less like a recommendation list and more like a personal cultural navigation tool. Step-by-step: 1. I import or paste my watched films and series. 2. I add a separate list of things I still want to watch. 3. I ask AI to analyze recurring themes and less obvious connections. 4. I build a descriptive taste profile rather than reducing everything to genres. 5. I keep confirmed preferences separate from hypotheses based on the watchlist. 6. I use my later reactions to refine the profile. 7. When choosing something to watch, I add the current context, including available time, mood, energy, desired emotional intensity, whether I want something comforting or challenging, and whether I want a movie or an episode. 8. I match that temporary context against my longer-term taste profile and the available watchlist.