AI & RAG

Stale documentation detector for Confluence + GitLab wikis

Weekly, samples answer-bot queries that returned low-confidence or no results, traces them to gaps or outdated pages in Confluence and GitLab wikis.

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionPull low-confidence queries from PostgresPostgreSQLPostgres
  • ActionFind closest Confluence + GitLab wiki pagesConfluenceConfluence
  • LogicClassify gap: missing, outdated, or false miss
  • ActionGroup and dedupe actionable gaps with OpenAIOpenAI
  • OutputFile a Linear issue per documentation gapLinearLinear

What it does

Closes the loop on your RAG system. Each week it reviews questions the answer bots couldn't confidently answer, uses OpenAI to classify whether the gap is a missing page or a contradicting/outdated one in Confluence or GitLab wikis, and files a Linear issue describing the gap with the failing queries attached.

When to use it

When you run grounded answer bots and want documentation to improve over time instead of decaying silently. Turns retrieval misses into a prioritized backlog for whoever owns the docs.

How it works

  1. 1A weekly schedule trigger starts the review.
  2. 2The flow pulls logged low-confidence and no-result queries from Postgres.
  3. 3For each, it retrieves the closest existing Confluence and GitLab wiki pages.
  4. 4A classifier decides: missing doc, outdated doc, or false miss (skip).
  5. 5Actionable gaps are deduped and grouped by topic.
  6. 6A Linear issue is created per gap with the failing queries and suggested owner.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect GitLabRepos, MRs, pipelines, registry.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Connect LinearIssues, projects, cycles, triage.
  6. 6
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  7. 7
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  8. 8
    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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