AI & RAG
Flag incomplete postmortems before they enter the knowledge base
Reviews each newly drafted postmortem against required sections, and if anything is missing or vague.
How it runs
The automated pipeline, trigger to output.
- TriggerPostmortem moves to In Review in NotionNotion
- ActionEvaluate required sections for presence and specificityOpenAI
- LogicBranch on whether all quality checks pass
- ActionTag the page Ready-to-Index in Notion when it passesNotion
- OutputDM the author the specific gaps in Slack when it failsSlack
What it does
Acts as a quality gate so the answerbot is never fed thin or vague postmortems. When a postmortem moves to In Review in Notion, the flow checks for required sections (timeline, root cause, impact, action items with owners) and judges whether each is specific enough to be useful later.
When to use it
Run it alongside your indexer to protect retrieval quality. A RAG answerbot is only as good as its corpus; this keeps junk out and nudges authors to fill real gaps while the incident is still fresh.
How it works
- 1A Notion status change to In Review triggers the flow.
- 2The page content is parsed into its required sections.
- 3The model evaluates each section for presence and specificity, returning a structured list of gaps.
- 4A logic step branches: if all checks pass, the page is tagged Ready-to-Index; if not, it stays in review.
- 5When gaps exist, a Slack DM to the author lists exactly what to fix, with the section names.
Set it up
What you configure once, before turning it on.
- 1Connect NotionPages, databases, comments.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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