CHATBOTS

/docs with low-confidence escalation to an expert

Answers a Slack /docs question from ReadMe, and when confidence is low, escalates by @-mentioning the on-call subject expert in the thread with a pre-drafted summary instead…

CategoryChatbots
Enginesim
Difficultyintermediate
Triggerwebhook
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSlack /docs slash command receivedSlack
  • ActionSearch ReadMe for relevant pagesReadMeReadMe
  • ActionDraft answer and confidence score with OpenAIOpenAI
  • LogicBranch on confidence threshold
  • OutputPost answer or expert-mention handoff to SlackSlack

What it does

Provides a `/docs` answer bot that knows when to defer to a human. It searches ReadMe and replies with a cited answer when coverage is strong. When the docs don't clearly answer the question, instead of hallucinating it posts a short summary of what was asked, what it found, and @-mentions the on-call expert for that topic area so a person can jump in.

When to use it

When wrong answers are costly and you'd rather route hard questions to an expert than risk a confident-but-incorrect reply. Keeps the convenience of self-serve docs while protecting answer quality.

How it works

  1. 1Slack sends the `/docs` slash-command payload to the webhook.
  2. 2ReadMe search retrieves the most relevant pages.
  3. 3OpenAI drafts an answer and returns a self-assessed confidence score.
  4. 4A logic branch splits on the confidence threshold.
  5. 5High confidence posts the cited answer; low confidence posts a handoff summary and @-mentions the on-call expert in the Slack thread.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect ReadMeAPI docs, changelog, auth.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect HTTP webhookTrigger any URL on agent actions.
  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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