Why? (Objective)

1. For business:

   Stale internal documentation that engineers continue to reference leads to integration errors, deprecated endpoint usage, broken internal tooling, and avoidable engineering time lost to debugging problems that stem from outdated instructions rather than actual bugs. As internal APIs iterate faster than documentation habits change, this creates a silent tax across engineering, repeated Slack questions, duplicated troubleshooting, and eroded trust in internal docs as a source of truth, pushing engineers to bypass documentation entirely in favor of reading source code or asking teammates directly, which doesn't scale as the org grows.

2. For users:

   Engineers currently have no signal, at the point of reading, that the internal doc they've opened may be superseded by a newer version maintained elsewhere in the org. They either already need to know a newer version exists, discover it by chance, or find out the hard way during code review, testing, or after an integration fails. This wastes engineering time, creates friction between teams (the doc owner vs. the engineer who followed stale instructions), and discourages engineers from trusting internal documentation as a first stop.

3. Scope note:

   This PRD is scoped to internal-facing documentation used within the company. Public/external developer-facing docs are explicitly out of scope for v1, though the detection approach should be architected to extend there later.

How do we measure success?

1. Associated OKR/Goal

   Improve internal developer productivity by reducing time lost to outdated or unreliable internal documentation.

2. Success Metrics

   <aside> 📈

   • Stale-doc reliance reduction: % decrease in active views/usage of documents that have been confirmed as superseded, after the warning is shown. Measured by: document_health_warning_shown → document_health_updated_doc_clicked conversion rate, with baseline established 2 weeks post-launch.
   • Redirect rate: % of engineers who click “View updated document” when shown the warning.
   • Owner confirmation rate: % of system-flagged potentially outdated documents that are confirmed by the document owner as requiring an update or having a newer document.
   • Stale-doc-caused incident reduction: Reduction in support/Slack questions, debugging issues, or bug reports attributed to outdated internal documentation, where such tagging or reporting exists. </aside>

3. Guardrail/Do not disturb metrics (These metrics shouldn’t be negatively affected)

   <aside> 📉

   • False-positive rate on warnings: % of warnings where the owner confirms that the document is still valid and requires no update. Must remain below 10% , false positives erode trust in Document Health and cause users to dismiss the feature.
   • Overall document search/open rate: Should not decline significantly; we don't want engineers avoiding internal documentation because of warning fatigue.
   • Warning dismissal rate: % of engineers who dismiss or ignore the outdated-document warning without opening the suggested document.
   • Document owner complaint/sentiment: Monitor owner feedback and complaints to ensure Document Health does not create excessive notification or maintenance burden. </aside>

Who are the users:

1. Engineer / Reader/ Primary User

   1. Persona

      Engineers who rely on shared internal documentation to understand processes, systems, APIs, or technical workflows.

   2. Problems we are solving [Needs, Problems, Jobs to be done]

   <aside> 📎

   • May continue using an older document without knowing that a newer version exists.
   • Has no reliable signal at the point of reading that the document may be outdated.
   • May discover newer documentation only through teammates, code review, testing, or other indirect means.
   • Needs to quickly identify and switch to the current source of truth. </aside>

   c. How do we know these problems exist (research)

   Engineers may continue relying on older internal documentation because they have no clear signal that a newer or more relevant version exists, leading to wasted time, incorrect implementations, or additional troubleshooting.

   Evidence to validate:

   <aside> 🧾

   • User interviews focused on how engineers discover, evaluate, and handle outdated documentation
   • Survey measuring how frequently engineers encounter outdated or conflicting documentation
   • Behavioral analysis of older documents to understand whether they continue to receive meaningful usage
   • Support / Slack / incident analysis to identify cases where outdated documentation contributed to implementation errors, debugging, or repeated questions </aside>

2. Document Owner / Maintainer / Secondary User

   1. Persona

      Employees or teams responsible for creating and maintaining shared internal documentation.

   2. Problems we are solving [Needs, Problems, Jobs to be done]

   <aside> 📎

   • May not realize that an older document is still being actively used.
   • Needs a simple way to validate whether a potentially outdated document is still relevant.
   • May need to point readers to a newer document or mark the old one as no longer relevant.
   • Needs the ability to change a previous decision if the document's status changes. </aside>

   c. How do we know these problems exist (research)

   Document owners may not realize that older documentation they maintain is still actively being used, making it difficult to know when it needs to be updated, replaced, or retired

   Evidence to validate:

   <aside> 🧾

   • Owner interviews focused on how teams currently maintain, update, and retire internal documentation
   • Survey measuring how often owners discover that older documentation is still being used
   • Behavioral analysis comparing document age, updates, and continued usage
   • Workflow analysis of how owners currently communicate newer documentation or mark old documentation as no longer relevant </aside>

Solution:

1. Brief of the solution. Document Health detects shared internal documents (starting with API documentation) that show signs of being outdated and still in active use. Rather than declaring a document outdated automatically, the system routes the decision through a human owner this keeps judgment where it belongs and avoids false positives reaching end users. If the original creator is unreachable or doesn't respond, the flow escalates to a fallback contact so the doc doesn't stay unresolved indefinitely. Detection also isn't a one-time check: documents are periodically re-evaluated on a company-configurable schedule, so a doc marked "still relevant" today can be revisited later if circumstances change.

   v1 scope note: Document similarity detection uses manual owner-linking only owners proactively link a newer document when they confirm one exists. Automated similarity detection (embeddings-based cross-document matching) is explicitly scoped for v2.

2. Detection signals (v1)

   a. Document Age: Last modified timestamp (metadata, already available)

   b. Continued Usage: View counts, unique viewers, last-accessed timestamp (requires access to platform analytics/audit APIs)

   c. Manual owner-linking: When owner confirms a document is superseded, they manually link the newer document (replaces automated similarity matching in v1)

3. Other alternatives are considered with prioritization metrics.

   Alternative	Description	Why not chosen for V1
   Fully automated detection	System detects potential stale documents and directly warns users without owner confirmation.	Higher risk of false positives eroding trust in the warning; no accountability loop with document owners.
   User-reported staleness	Readers can flag a document when they believe it is outdated.	Relies on reader initiative. the same passive problem we're trying to solve. Readers often don't know that a newer document exists.
   Version-linking only	Document owners manually mark old documents as superseded and link to the newer version.	Simple and low-risk, but purely opt-in. It doesn't catch documents that silently become stale when no one proactively links a replacement.

4. Non-Goals (v1).

   1. Public/external-facing documentation : this feature applies to internal docs only; external developer docs are explicitly out of scope.
   2. Automated similarity detection : v1 uses manual owner-linking only. Auto-detection via embeddings is a v2 feature.
   3. Real-time detection : detection runs on batch re-scan intervals only (configurable, default 90 days), not real-time.
   4. Automatic deletion of documents : Document Health can only mark docs as "Irrelevant"; actual deletion remains a separate, deliberate action outside this feature.
   5. Cross-organization document sharing : scoped to single-organization internal documents.

Product flow (Details of the feature/product):