AI & RAG

Grounded Coda Answer Bot in Slack with Row-Level Citations

Answers employee questions in Slack by retrieving relevant rows from a Coda knowledge hub, drafting a grounded reply with OpenAI.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSlack mention or slash command with a questionSlack
  • ActionRetrieve top candidate rows from Coda knowledge tableCodaCoda
  • LogicBranch: no row above relevance threshold → post not-found reply
  • ActionDraft answer grounded only in retrieved rowsOpenAI
  • OutputPost threaded Slack reply with row-level citation linksSlack

What it does

Turns a Coda knowledge hub into a self-serve answer bot for your team. When someone asks a question in a Slack channel, the workflow retrieves the most relevant rows from your Coda doc, generates an answer strictly grounded in those rows, and posts the reply with links back to each source row. If nothing relevant is found, it says so instead of guessing.

When to use it

Use it when your team keeps asking the same policy, process, or product questions and the authoritative answers live in Coda. Ideal for ops, HR, and support teams who want fast answers without anyone losing trust in a hallucinated reply.

How it works

  1. 1A Slack mention or slash command fires the workflow with the user's question.
  2. 2The bot searches the Coda knowledge table and pulls the top candidate rows.
  3. 3A logic step checks whether any row clears the relevance threshold; if none do, it posts a polite "not found" reply.
  4. 4OpenAI drafts an answer constrained to the retrieved row text, citing each row it relied on.
  5. 5The reply is posted back in-thread with row titles and deep links to the exact Coda rows.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect CodaDocs, packs, automations.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    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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