Duolingo Expert Content Engine — Map of Content

Most companies have real expertise and no way to publish it repeatably. They produce campaigns, not programs: a burst of posts, an empty topic list, silence. Duolingo solved the supply problem structurally — the questions users already send in become the topic queue, a named credentialed expert answers them on a fixed cadence, and the submission address at the bottom of every instalment refills the queue.

The second half is less obvious and buys more trust than the answers do: marking in public where the evidence stops. Defining the unit before publishing a count. Naming the motivated-reasoning risk before answering a contested question. Saying plainly that nobody knows.

This skill is a graph. Scan the descriptions, follow only the [[wikilinks]] you need.

The intake loop

• [[references/reader-questions-as-pre-validated-demand]] — an inbound question is demand someone already paid to express; run it verbatim.
• [[references/the-inbox-is-the-cta-and-the-backlog]] — the submission address is both the call to action and next week's editorial queue.
• [[references/credentialed-byline-and-named-outsiders]] — the named credential is the cheapest proxy for accuracy, and hiring an outsider proves you know your limits.

Turning the audience into a supply line

• [[references/promote-corrections-to-their-own-instalment]] — corrections are the highest-intent signal an audience produces; never patch them silently.
• [[references/solicit-tactics-credit-them-add-the-mechanism]] — users find affordances your telemetry cannot see; credit them by name and explain why each works.