AI & RAG

Weekly Report of Technical Questions Your Looms and Docs Cannot Answer

On a weekly schedule, replays the past week of SE technical questions against the Loom and Confluence corpus and reports which topics had no grounded answer.

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule fires
  • ActionCollect past week's technical questions from SlackSlack
  • ActionScore corpus coverage per question against Loom and ConfluenceLoomLoom
  • LogicCluster low-coverage questions into gap topics
  • ActionWrite prioritized gap report via OpenAIOpenAI
  • OutputPost report to Slack and save backlog page to ConfluenceConfluenceConfluence

What it does

Once a week this workflow gathers every technical question your SE team fielded, re-runs each against your indexed Loom walkthroughs and Confluence docs, and surfaces the questions that came back with weak or no grounding. The result is a ranked list of knowledge gaps to record a Loom about or document next.

When to use it

Use it to make your knowledge base improve itself. Instead of guessing what to document, you get hard evidence of the questions your corpus repeatedly fails to answer.

How it works

  1. 1A weekly schedule triggers the flow.
  2. 2An action collects the past week's technical questions from the SE Slack channel.
  3. 3For each question, the retriever queries the Loom and Confluence corpus and scores answer coverage.
  4. 4A logic step clusters low-coverage questions into recurring gap topics.
  5. 5OpenAI writes a prioritized gap report with suggested Loom or doc topics to create.
  6. 6The report is posted to Slack and saved as a Confluence page for the team's content backlog.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect LoomVideo transcripts, libraries.
  3. 3
    Connect ConfluenceSpaces, pages, blueprints.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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