Set a Doc Freshness Policy Before Your AI Lies

The problem is never the AI — it's the doc

When a customer asks your AI agent about a feature you deprecated six months ago and gets a confident, sourced answer pointing to a dead help article, the instinct is to blame the model. The real culprit is the doc that stayed live after the feature died.

Chattering cites whatever your help center contains. If your help center contains stale truth, Chattering cites stale truth — accurately, politely, and to the wrong end. A doc freshness policy closes that gap before it embarrasses you in a ticket.

What a freshness policy actually is

It's a lightweight governance document — one internal page is enough — that answers three questions for every article in your help center:

• Who owns it? One named person, not a team.
• When does it expire for review? A fixed date, not 'eventually'.
• What event triggers an immediate pull? A product change, a pricing update, a deprecation.

Without those three answers, articles accumulate like browser tabs. Nobody closes them; nobody is sure if they still apply.

Assign a single owner per article

Shared ownership is no ownership. When you assign an article to 'the support team', every member of the support team assumes someone else is watching it.

The owner doesn't have to be a writer. It can be the PM who shipped the feature, the engineer who owns the API, or the CSM who fields the most questions about it. What matters is that one person gets a calendar reminder and feels the embarrassment if the article goes wrong.

In Chattering's help center, you can tag each article with an owner field in the metadata. Set it when you publish, not retroactively — retroactive tagging is a project that never finishes.

Set a default review interval, then override it by risk tier

Not every article needs quarterly review. A conceptual explainer about how webhooks work ages more slowly than a page detailing your current plan limits.

A practical starting framework:

• High-volatility docs (pricing, feature availability, API rate limits): review every 60 days, pull immediately on any product change.
• Procedural docs (how to connect an integration, how to invite a team member): review every 90 days.
• Conceptual docs (what is a workspace, how does context work): review every 180 days.

Build these intervals into your project management tool as recurring tasks assigned to the named owner. If your team uses Linear, GitHub Issues, or Notion, a templated recurring task takes about five minutes to set up and runs itself.

Define your sunset triggers explicitly

A review interval catches gradual drift. Sunset triggers catch sudden breaks.

A sunset trigger is any event that should immediately kick off a doc review — or a doc pull. Common ones for SaaS support teams:

• A feature is deprecated or merged into another feature.
• Pricing changes.
• A third-party integration changes its auth flow or UI.
• Your product renames a core concept (say, 'workspaces' become 'organizations').

The way to operationalize this is to add a docs-review step to your deprecation and release checklists. Every time engineering closes a deprecation ticket, the ticket description should include a line: 'Help center articles affected: [list]'. The owner reviews or pulls those articles before the deprecation ships, not after the first confused support ticket.

How to handle articles that are expired but not wrong yet

This is the uncomfortable middle ground. The feature still works; you just haven't updated the screenshots since a UI refresh. The article isn't lying — it's just slightly out of date.

The right call is to mark the article as under review in your help center and hide it from Chattering's index until the update is done. In Chattering, you control which articles are included in the retrieval pool. An article that's 'probably fine' is still a citation risk. Pull it from the index, update it, republish. The review cycle for an article you already understand should take less than 30 minutes.

Audit your current help center before you set policy

If you're reading this with a help center that has grown organically for two or more years, start with a one-time audit before you put governance in place.

Export your article list with last-edited dates. Sort by oldest. Any article untouched for more than a year gets one of three labels: keep and update, redirect to a newer article, or unpublish. Do this as a team session — it takes a half-day and surfaces disagreements about what's actually true in your product right now.

Once the backlog is clean, the ongoing policy is easy to enforce because the starting state is trustworthy.

The compounding benefit you're really after

A freshness policy doesn't just protect customers from bad AI answers. It also makes every future AI answer more confident and more accurate — because Chattering is only drawing from a pool of articles your team has explicitly vouched for. The quality of AI support is a direct function of the quality of the knowledge base it indexes. Governance of one is governance of the other.