Good Enough at Scale: Documentation Infrastructure Under Capacity Constraints
summary
Two writers support nine products and find writing itself is rarely the bottleneck; discovery, verification, reviews, and follow-up take most time. The team builds GitHub workflows, AI-assisted drafting, link checkers, stale-content detectors, review gates and migration scripts that detect PRs affecting docs and surface alerts, but every prompt, workflow and check needs an owner and upkeep. A four-tier risk model plus a simple gap register and user signals (support tickets, 40 empty searches) drive what gets fixed next.
AI-written summary · about
chapters (21)
- 0:00Scaling documentation challenges
- 1:07Product complexity vs team capacity
- 3:15Writing speed won’t fix docs
- 6:08Good enough docs with quality bars
- 7:33Risk tiers for docs gaps
- 10:02Sustainable docs infrastructure
- 12:33AI can’t replace human docs judgment
- 14:42Costs of docs automation
- 15:48Gaps register for decisions
- 17:56User signals guide docs fixes
- 19:22Playbook for good enough docs
- 20:48Summary and takeaways
- 21:30Q&A: Risk vs capacity
- 22:12Q&A: Risk-based testing overlap
- 22:54Q&A: Communicating limits
- 23:59Q&A: Docs tool budget
- 24:40Q&A: Competing priorities
- 25:01Q&A: Speed vs accuracy
- 26:04Q&A: Dev-written docs oversight
- 26:45Q&A: Automation vs team growth
- 27:27Q&A: Advocating for docs
related talks
39mThe Documentation Dream: One Team, No Silos, Shared Goal
25mBuilding Docs like Code: Continuous Integration for Documentation
35mFrom Code to Clarity: Collaboration with Devels when Writing Product Documentation
59mTransforming your Documentation Process
37mTaking a step back: Analysing your documentation
30mBuilding technical documentation as a product at scale
44mCreating a Culture of Documentation
41mLong-term Documentation Maintenance