AI & RAG
Audit answer-bank citation freshness against live Confluence docs
Walks every entry in the Postgres answer bank, re-checks the Confluence page each answer cites, and flags or quarantines entries whose source doc changed, moved, or was deleted…
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule: nightly
- ActionPage through active answer_bank entriesPostgres
- ActionFetch cited Confluence page + versionConfluence
- LogicCompare stored vs current version hash
- ActionLLM judges if answer still matches sourceOpenAI
- ActionFlag failing entries as stale in PostgresPostgres
- OutputPost freshness summary to SlackSlack
What it does
Keeps your RAG answer bank honest. Each stored answer is linked to a Confluence source page; this workflow periodically re-reads those pages, compares the current content against what the answer claims, and uses an LLM to decide whether the citation is still valid. Entries backed by changed or missing docs are marked stale so retrieval can skip them.
When to use it
When support docs evolve faster than your knowledge base and you risk serving confidently wrong answers. Run nightly to guarantee every retrievable answer still points at a live, consistent source.
How it works
- 1A nightly schedule starts the audit.
- 2The workflow pages through all active entries in the Postgres answer bank.
- 3For each entry, it fetches the cited Confluence page by ID and reads its current version and last-updated date.
- 4A logic step compares stored vs. current version hashes to find pages that changed or 404'd.
- 5For changed pages, an LLM judges whether the answer still matches the source.
- 6Entries that fail verification are flagged `stale` in Postgres and a freshness summary is posted to a Slack review channel.
Set it up
What you configure once, before turning it on.
- 1Connect PostgresAny Postgres URL — query, write, migrate.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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