AI & RAG

ADR Intake Triage with Grounded Reply

When a new decision proposal lands in Notion, retrieves prior ADRs on the same topic, drafts a grounded comment citing related and conflicting decisions.

CategoryAI & RAG
Enginepaperclip
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew proposal page created in NotionNotionNotion
  • ActionRetrieve related existing ADRs by topicNotionNotion
  • LogicClassify matches as supporting, superseded, or conflicting
  • ActionDraft cited reviewer comment with recommendationOpenAI
  • OutputWrite comment to Notion and tag for approvalNotionNotion

What it does

Makes new architecture proposals start from what you've already decided. When someone files a decision proposal in a Notion database, it searches your existing Architecture Decision Records for prior art on the same topic, then drafts a grounded reviewer comment: which past ADRs support the proposal, which it would supersede, and which it conflicts with — every reference cited by number.

When to use it

When proposals get written in a vacuum and reviewers waste cycles digging up old decisions by hand. Ideal for teams running an RFC/ADR intake queue who want context attached automatically before a human weighs in.

How it works

  1. 1A new page is created in the Notion ADR-proposals database.
  2. 2The proposal text and topic are extracted.
  3. 3Related ADRs are retrieved from the existing knowledge base by semantic match.
  4. 4A logic step classifies each match as supporting, superseded-by-this, or conflicting.
  5. 5OpenAI drafts a reviewer comment citing each ADR and recommending accept, revise, or reject.
  6. 6The draft is written back as a Notion comment and tagged for human approval.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect NotionPages, databases, comments.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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