AI & RAG
Nightly coverage-gap audit comparing real questions against ReadMe docs
On a schedule, replays recent developer questions against the ReadMe corpus, flags ones with no grounded answer.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule fires audit run
- ActionRead recent logged questions from PostgresPostgres
- ActionScore each question against ReadMe indexReadMe
- LogicBucket below-threshold questions as gaps and cluster
- ActionSummarize each gap into a proposed doc titleOpenAI
- OutputFile one Linear ticket per coverage gapLinear
What it does
Finds where your documentation is missing. Each night it pulls the day's logged developer questions, runs each one through retrieval, and identifies questions that returned no confidently grounded answer — the real coverage gaps — then files them for the docs team.
When to use it
When you want your docs roadmap driven by what developers actually ask rather than guesswork. Best for portals where the answer endpoint logs every question and you want a steady backlog of high-impact doc work.
How it works
- 1A nightly schedule triggers the audit run.
- 2Recent logged questions are read from Postgres.
- 3Each question is matched against the ReadMe vector index to measure best-match retrieval score.
- 4A logic step buckets questions below the grounding threshold as uncovered gaps and clusters near-duplicates.
- 5The model summarizes each gap cluster into a proposed doc title and scope.
- 6One Linear issue per gap cluster is created for the docs team, tagged with question frequency.
Set it up
What you configure once, before turning it on.
- 1Connect PostgresAny Postgres URL — query, write, migrate.
- 2Connect ReadMeAPI docs, changelog, auth.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect LinearIssues, projects, cycles, triage.
- 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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Run this workflow in your colony.
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