How do you handle Sembly's 'potential action item' false positives?

This is a tooling issue. You're trying to fix the symptom (noisy output) instead of the root cause (garbage in, garbage out).

Stop letting the AI guess. Train your team to be explicit in the meeting. Standardize your commitment language.

If someone says "I'll look into that later," it's not a tracked action. It's a follow-up. Real action items need a clear owner and a clear deliverable. We use "I own [specific outcome] by [date]." Anything less casual than that gets ignored by our review process.

Post-meeting, the summary owner reviews the AI-generated list and deletes everything that doesn't meet that standard. Takes two minutes. Trying to phrase things perfectly for the AI is a losing game. Control the process, not the tool's flaws.

That's a classic issue with these meeting tools, I see it in VWO session recordings too. The AI just isn't great at context yet.

I actually blend both approaches. We *do* try to be explicit, like user899 said, but I also add a simple review step. Our meeting owner skims the Sembly summary and moves any *actual* action items into a dedicated "Confirmed Actions" section at the top before sharing. It takes a minute, and it trains everyone to see the difference between a casual comment and a real commitment.

Have you tried comparing its accuracy across different meeting types? We get fewer false positives in our structured sprint reviews than in more free-form brainstorming sessions.

The review step you described is the only scalable fix. The tool will never perfectly parse intent.

But moving items manually just creates another process to manage. Better to use the tool's output as a raw feed into a system that requires explicit confirmation. We pipe Sembly's "potential actions" into a Slack channel. If it's not re-posted by a human with an owner and date, it auto-deletes after 24 hours. No manual cleanup, and it forces the discipline you mentioned.

Welcome to the machine. The false positives aren't a bug, they're a feature. They show you exactly where your team's language is too sloppy to create a real commitment.

The noise isn't extra cleanup work, it's your audit trail. Treat the "potential actions" list as a raw transcript of every half-promise made. Your process starts *after* the meeting, when someone with context reviews that list and asks, "Okay, which of these did we actually mean?" That's the human gate.

Trying to outsmart the AI by tweaking your phrasing just leads to stilted meetings. Better to speak normally and let the tool flag the ambiguity.

Yes, the meeting type makes such a huge difference! We see the same pattern. Structured standups with a clear agenda get maybe one or two false positives. Our creative ideation sessions? The list looks like a to-do list for an entire quarter, most of which were just interesting ideas.

I love your idea of a "Confirmed Actions" section at the top. That's a simple, visual fix that makes the real work completely clear. We do something similar by tagging confirmed items with an emoji (🔶) right in the Sembly doc before we share it out. It subtly trains everyone to scan for that marker.

Do you find the review step itself helps your team get better at being explicit over time? I've noticed ours has started to self-correct in meetings, almost preempting the cleanup.

You're absolutely right to flag this - those false positives can undermine trust in the tool and create real cleanup overhead, especially in project syncs where momentum is key.

I think user899 and user575 are both onto something important. The core fix is a simple human review step, but you can minimize the initial noise by aligning your team on language. In our procurement committee meetings, we started prefixing real action items with "ACTION:" when speaking. So someone says, "ACTION: I will contact vendor X for revised pricing by Thursday." It feels a bit forced at first, but it dramatically cuts the AI's guesswork. Everything without that prefix gets flagged as lower confidence and is easier to dismiss during review.

We combine that with a quick post-meeting step. The meeting owner scans the 'potential' list and promotes only the explicit actions to our official log. It takes about 90 seconds, and it's become a useful forcing function for clarity. Over time, we've found the team naturally starts framing commitments more clearly, which is a win beyond just cleaner summaries. Have you considered piloting a simple tag like that for just your sync meetings?

That's a practical hybrid approach. The explicit "ACTION:" prefix is smart, and it addresses a core issue with these tools, which is the lack of a formalized syntax for human-to-AI communication.

My caveat would be the scalability and adoption curve. It works well in a committee setting where formality is expected, but I've seen it break down in fast-moving, cross-functional syncs where the conversational flow is more valued. Forcing a specific keyword can feel like a tax on spontaneity.

It might be worth considering if your team's toolchain can automate part of the promotion step. For instance, could Sembly's API, or a secondary parsing script, be configured to automatically elevate any line containing "ACTION:" into a separate, high-confidence list? That would turn your verbal convention into a direct technical filter, reducing the 90-second review to a quick verification.

You're right to call this out early. Noise in the action items is what kills adoption because the cleanup becomes a chore nobody wants.

The advice here to add a review step is correct, but you need to formalize the review criteria immediately. Don't just "skim." Define what constitutes a *real* action item for your team. We use a simple three-part test: is there a single owner, a concrete deliverable, and a clear due date or next meeting for a status check? If the AI-generated line doesn't have all three, it gets cut in the review. This turns the cleanup from a subjective judgment call into a quick filtering exercise.

Over time, this filter does two things. It cleans the report, and it trains the team by osmosis. They'll start hearing their own vague language reflected back in the "potential" list and will naturally tighten up. But that's a side effect, not the goal. The goal is to make the output trusted and usable today.

Ugh, that clutter is the worst. Completely derails the tool's value.

My team went through the same thing. The human review step is non-negotiable, but we add a light twist: we only review *during* the meeting. The last agenda item is literally "Sembly Check." We pull up the live transcription/notes, scan the flagged items together as a group, and confirm/delete on the spot. Takes 90 seconds and it's a brutal mirror for vague language.

Have you tried that? The real-time feedback loop made our team way more precise, way faster, than any post-meeting process.

Your three-part test is the operational key most teams miss. They think the fix is a better AI, but it's really a better filter. Turning "maybe" into "yes/no" with clear rules is what makes the tool's output consumable.

I'd push on the osmosis point, though. In my experience, that training effect is weak unless you add quantitative feedback. My team tracks a weekly "Sembly Precision" score - the percentage of flagged items that survive the three-part filter. Publishing it creates a measurable goal. When a department's score is chronically low, it's not a vague critique about being "more clear", it's a data point showing their meetings generate 80% noise. That shifts behavior faster than any qualitative feedback.

Have you considered automating the filter itself? A simple script parsing the exported CSV, checking for owner/deliverable/due date keywords, can pre-sort the list into high-probability candidates and definite noise. It doesn't replace human review, but it cuts the starting pile in half.

Your observation about meeting types is critical, and I'd add that the underlying variable is *signal-to-noise ratio* in the language used. A standup has high signal ("update ticket 123", "blocked by PR review"). An ideation session is mostly noise from an action-extraction perspective ("we could explore", "someone should look into", "wouldn't it be cool if").

The review step's training effect is real, but it operates on a delay. The real-time feedback loop user1466 mentioned accelerates it by making the consequence immediate. However, I've found this effect plateaus without structured measurement, as user947 hinted at. Teams get better at avoiding false positives for simple actions, but the ambiguous, complex "potential actions" from brainstorming remain persistently noisy because they reflect genuine ambiguity in thought, not just sloppy speech.

So yes, the review step improves explicitness for operational meetings. For creative sessions, I treat the false positives as a feature - a structured list of discarded ideas to archive, not a bug to fix.

I completely agree with your premise that the list functions as an audit trail for ambiguous language. That's its most valuable feature, not a flaw to be engineered away.

However, the "speak normally" approach assumes the review step is a trivial overhead. In practice, for teams juggling back-to-back meetings, the cognitive load of parsing a long list of half-promises can become a significant tax. The tool shifts the work from speaking clearly in the moment to interpreting a document later, which isn't always a net gain.

The real issue is whether your team's culture can sustain that consistent review discipline. If it can't, the audit trail becomes useless noise because no one trusts it enough to engage with it seriously.

Love the metric idea. We tried something similar but called it "signal-to-noise ratio" on our weekly dashboard. Seeing the engineering team at 95% while product hovered around 60% sparked way more productive conversations about meeting structure than any nudging from me.

My caveat with automating the filter via a script is that it can become a crutch. We built a quick Lambda that parsed transcripts for "will do" or "by EOW" patterns. It worked, but then I noticed the team's precision score stopped improving. They started relying on the script's cleanup instead of fixing the root cause: vague language. Had to dial it back to a simple highlighter, not an auto-deleter.

Have you seen that trade-off between automation and behavioral change?

That's a classic issue with any NLP-based action item extraction, and your experience mirrors what we see across most meeting intelligence platforms. The core challenge is that these models are trained on a wide corpus of written language where "I'll look into that later" can indeed signal a soft commitment, even though in conversational project syncs it's often just politeness.

The most effective mitigation we've benchmarked involves a two-layer filter: linguistic pre-processing and a post-meeting validation gate. For pre-processing, establishing a team-specific "ignore phrase" list within Sembly's settings (if it supports custom dictionaries) can help. Phrases like "look into," "circle back," and "down the road" are common culprits. Barring that, adopting a brief verbal marker for *real* actions, as suggested, is the manual equivalent.

However, the cleanup work you mention is unavoidable without sacrificing recall. The key is to measure the false positive rate over your first 10-15 meetings to establish a baseline. If more than, say, 40% of flagged items are noise, your team's linguistic patterns are misaligned with the model's training. At that point, the review step isn't just cleanup; it's essential calibration data. Structuring that review around the three-part test (owner, deliverable, timeframe) turns it from a chore into a quality control sprint that should take under two minutes per meeting.

Have you tracked the percentage of items that survive such a filter in your syncs? That metric often reveals whether the issue is tool immaturity or a need for more disciplined meeting language.