Meetings generate lots of text but too few clear next steps. Rather than asking an LLM for a vague summary, feed it the transcript with a focused goal: extract discrete action items, assign owners (if mentioned), estimate due dates or urgencies when possible, and return a machine-readable list you can import into your task tracker.
Prep the transcript
Before you call the model, clean the input: include speaker labels and timestamps if available, remove filler noise that confuses intent (“um,” “like”), and split very long transcripts into topic-based chunks. Smaller, coherent chunks give the model better context and reduce hallucination risk from unrelated parts of the meeting.
Use structured prompts and templates
Ask the LLM to output a specific format (JSON, CSV, or simple YAML) with fields like “task_text”,”owner”,”due_suggestion”,”priority_reason”,”source_timestamp”. Example instruction: “From the following text, extract action items as JSON objects. Populate owner when someone is explicitly assigned; if no owner is given, leave null. Include the timestamp where this request was made.” A clear format makes downstream automation reliable.
Prioritize and add confidence signals
Tell the model how to prioritize (e.g., deadline, risk, dependencies) and ask it to include a confidence score or short justification for each suggested priority. That way you can surface high-confidence actions automatically and flag lower-confidence items for human review.
Validate and integrate
Don’t rely on the output verbatim. Run simple validation checks (owners match participant list, due dates are reasonable, no empty tasks) and present low-confidence items back to meeting participants for confirmation. Also consider privacy: redact sensitive data before sending transcripts to third-party LLMs or use on-premises models if confidentiality is required.
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