AI & RAG

Detect Documentation Gaps from Unanswerable Front Tickets

When the knowledge agent can't find a confident answer for a Front ticket, it logs the question, theme, and ticket link as a row in a Coda content-gap backlog so the docs team…

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Front ticket receivedFront
  • ActionSearch ReadMe and score retrieval confidenceReadMeReadMe
  • LogicBranch: end run if confidence clears threshold
  • ActionSummarize question into topic and categoryOpenAI
  • OutputAppend gap row to Coda content backlogCodaCoda

What it does

This workflow turns support failures into a prioritized documentation roadmap. Whenever an incoming Front ticket can't be answered from existing ReadMe content above a confidence threshold, it records the unanswered question, a normalized topic label, and the ticket link in a Coda backlog table — building a data-driven list of the docs your customers actually need.

When to use it

Run this when you want your content team to write articles based on real demand instead of guesswork, or to measure how often your docs fail to cover live questions. Pairs well with a citation-reply workflow that handles the answerable tickets.

How it works

  1. 1A new Front ticket triggers the flow and the question text is extracted.
  2. 2The agent searches ReadMe for passages and scores retrieval confidence.
  3. 3A branch checks the score: if it clears the threshold the run ends (another flow handles the reply).
  4. 4For low-confidence tickets, OpenAI summarizes the question into a one-line topic and category.
  5. 5The gap is appended as a new row in the Coda content backlog with the ticket link and timestamp.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect ReadMeAPI docs, changelog, auth.
  3. 3
    Connect CodaDocs, packs, automations.
  4. 4
    Connect OpenAIModels, embeddings, files.
  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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