AI & RAG

Auto-Draft an ADR from a Large MR Description

When a large merge request is opened, drafts a candidate architecture decision record grounded in the MR description and related prior ADRs.

CategoryAI & RAG
EngineSim + Paperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitLab MR openedGitLabGitLab
  • LogicContinue only for large or labeled MRs
  • ActionFetch MR description and related ADRsConfluenceConfluence
  • ActionDraft structured candidate ADROpenAI
  • OutputPost draft to Slack for approvalSlack

What it does

Large MRs usually encode a real architecture decision that never gets written down. When an MR exceeds a configurable size or carries a decision label, this flow reads its description and retrieves related prior ADRs for consistent context and numbering, then drafts a full candidate ADR in your house format (context, decision, consequences, related decisions). It posts the draft to Slack for a human to approve before anything is filed.

When to use it

Use it to capture decisions at the moment they are made, while the author still has the reasoning fresh, instead of reconstructing it months later. The human-approval step keeps quality and authorship under control.

How it works

  1. 1A GitLab webhook fires when an MR is opened.
  2. 2A logic step continues only for large or decision-labeled MRs.
  3. 3The MR description is fetched and related ADRs are retrieved from Confluence for context.
  4. 4An OpenAI call drafts a structured ADR grounded in those sources.
  5. 5The draft is posted to Slack with approve and edit actions for review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitLabRepos, MRs, pipelines, registry.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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