tossJun 23, 2026

6. Beyond Tools: Standards and Responsibility (opens in new tab)

Toss’s commerce domain found that reliable organizational knowledge cannot be created by writing more documents or adding automation alone. Sustainable knowledge management requires clear standards for what should be documented, who owns it, how it is maintained, and which sources can be trusted. The proposed solution combines AI-assisted documentation with domain-level responsibility and company-wide governance. ## The Limits of Writing Alone - A commerce wiki consolidated terminology, onboarding material, code references, and policy documents. - This reduced confusion over terms such as “seller” and “store” and gave teams a shared starting point. - However, product and policy changes happened faster than one Technical Writer could document them. - Important knowledge also appeared in policy changes, temporary experiments, and chat discussions that were difficult to track manually. ## Why Culture and Participation Were Not Enough - The team promoted documentation through: - A weekly “Commerce Wiki News” newsletter - A policy-question channel and bot - AI documentation workshops - A documentation guild - These efforts increased requests, wiki usage, and adoption of official terminology. - Participation rarely continued beyond an individual’s first document because documentation was not part of normal work priorities. - Writers lacked guidance on: - What information to preserve - How much detail to include - Which audience to target - How to verify whether a document was correct - Documentation became sustainable only when it was treated as a team responsibility embedded in existing workflows. ## AI Automation Reveals the Governance Problem - AI now creates draft documents nightly from two signals: - Product deployment and policy-change announcements - Questions that the commerce Q&A bot cannot answer - AI gathers supporting context and produces drafts, while humans verify the evidence and approve them. - This removes the burden of starting documents from a blank page. - Automation also exposed new problems: - Duplicate or overlapping documents - Unclear authoritative sources - Outdated policies being used in bot answers - Difficulty distinguishing current policies from completed experiments - Automation can collect and draft information, but it cannot decide who owns a policy or whether a document should still be trusted. ## Knowledge Standards and Governance - The focus shifted from “How do we create more documents?” to “How do we create knowledge people can trust?” - Toss’s knowledge-management standards state that teams should: - Preserve recurring questions, important decisions, and information needed by newcomers. - Organize knowledge so both people and AI can find it. - Connect documents to work tools such as Q&A bots and GitHub. - Assign owners and review cycles to keep information accurate and current. - Possible classification systems include: - **Technical layers** for teams with clear data or system flows - **Service domains** for teams responsible for multiple service areas - **Functional units** for systems with distinct feature boundaries - Information becomes organizational knowledge only when it helps people understand situations and make better decisions, with sufficient context and verification. ## The Role of the Knowledge Committee - The Knowledge Committee defines and maintains company-wide documentation standards and resolves conflicts between organizational rules. - Unlike a voluntary guild, it has designated members with decision-making authority. - Governance operates at two levels: - The Technical Writing Chapter manages shared standards for sources, ownership, document status, and lifecycle. - Individual domains decide how those standards apply locally, including ownership, update schedules, and retirement rules. - This balance prevents both inconsistent practices across teams and overly centralized rules that ignore local realities. - For example, commerce teams may need separate handling for permanent deployments and temporary experiments so expired policies do not remain authoritative. The practical recommendation is to treat knowledge management as an operating system for the organization, not a documentation project. AI can reduce the effort of capturing knowledge, but clear ownership, review processes, lifecycle rules, and governance are necessary to keep that knowledge reliable and useful.

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tossJun 22, 2026

Why Does Our Team’s Documentation Fail? (2) (opens in new tab)

Documentation succeeds not through individual resolve, but through centralized knowledge, clear purpose, and systems that reduce the fear of being wrong. Interviews across a commerce domain and a server-development chapter show that documentation strategies must match an organization’s existing maturity, audience, and work patterns. AI lowers the cost of writing and sharing knowledge, while also requiring more deliberate document organization. ## Lessons from Early Attempts - The commerce wiki was initially designed as a self-service platform supported by workshops and guilds. - Workshops could encourage a first contribution, but sustaining second, third, and later contributions was difficult. - In the Ads domain, documentation was already strong, so the better approach was to respect existing conventions and help people locate knowledge rather than create another system. - Organizations with little documentation need to build foundational knowledge; organizations with mature documentation need better discovery and maintenance. ## Reducing the Fear of Asking and Writing - Developers often avoid asking questions because doing so publicly reveals what they do not know. - They may also hesitate to publish documentation because they fear their knowledge could be inaccurate. - The team addresses this through: - **“Lee’s Development Consultation Week,”** which normalizes questions and encourages teammates to answer questions Lee cannot address. - **A daily knowledge bot, “Ha,”** which shares short server-development tips automatically. - Correcting or adding to an existing shared post feels easier than writing a complete document from scratch. ## How AI Has Changed Documentation - AI makes it faster to create initial drafts and distribute knowledge through chatbots. - It also enables measurement of knowledge flow, including: - The number of questions asked. - Whether teammates provide answers. - The quality and content of those answers. - The number of new documents and week-over-week growth. - These metrics reveal knowledge gaps and recurring questions without manually reviewing every channel. - AI also creates a need for more detailed internal context than human readers typically require. - Commerce therefore separates: - Central, human-friendly documentation managed by technical writers. - Team-repository documentation containing detailed, team-specific context useful to AI but unnecessary for everyone else. ## Shared Principles and Organizational Differences - Both domains and chapters should centralize knowledge and avoid spreading it across too many channels. - Domains typically: - Connect documentation to products and code. - Change rapidly. - Serve a broad and varied audience. - Chapters typically: - Document conventions, working methods, and professional knowledge. - Change more slowly. - Focus on productivity and capability development. - Have a clearer audience, such as a specific role or discipline. - Domain documentation should be understandable even to non-developers, since developers may also lack context outside their specialties. - Separating guides, capability-based policies, glossaries, and metrics helps different readers find documents suited to their needs. ## Where to Begin - First diagnose the organization’s current documentation maturity by asking what people do when they get stuck: - **Ask coworkers or search chat:** foundational documentation is largely missing. - **Search documents:** assess whether information is easy to find; fill gaps if searches fail. - **Ask an AI or bot:** evaluate whether answers are accurate and whether the underlying documents are complete, current, centralized, and sufficiently contextualized. - Define the specific problem driving documentation, rather than starting with a vague goal. - Begin with a focused need, such as creating a glossary for inconsistent terminology or building references for sharing knowledge with other teams. The practical recommendation is to centralize knowledge, identify the organization’s biggest documentation gap, and build a low-friction system where people can contribute, correct, and consume information without relying solely on personal effort.

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