How I Document 50+ Features Monthly Without Losing My Mind
November 2023: I had 47 feature documentation requests in my backlog. Engineering shipped faster than I could document. Product complained docs were always out of date. Support escalated because customers couldn't find answers. I was working 60-hour weeks and falling further behind. The breaking point came when I realised I was treating each doc as a bespoke project requiring heroic effort—when 80% followed identical patterns. I systematised: templates for every doc type, structured interview process for SME engagement, review orchestration handling multiple stakeholders in parallel, automated quality gates. Within 3 months, I was consistently documenting 50+ features monthly in 40-hour weeks, with faster turnaround and higher quality. Here's the complete system, with every template and process diagram you need.
The 50-Feature-Per-Month Reality
Volume is the defining challenge of modern technical writing. In an active engineering organisation, you're not dealing with a handful of features per quarter—you're facing a relentless tide of shipping code that needs user-facing documentation yesterday.
The numbers tell the story. Engineering ships two to three features daily during active sprints. Product launches capabilities faster than docs can keep up. Support needs answers for customers immediately—not next week, not tomorrow, now. Documentation lag translates directly into customer confusion, support tickets, and ultimately churn. When someone can't figure out how to use a feature, they don't blame the documentation team. They blame the product.
Most technical writers approach this challenge with what I call the unsustainable heroic model. You treat each doc as a custom project. You reinvent the structure every time, staring at a blank Confluence page wondering where to start. You chase SMEs reactively, sending plaintive Slack messages hoping someone will find fifteen minutes to explain their feature. Reviews happen serially—wait for engineering approval, then wait for product, then wait for legal. The result is an 8-day average cycle time, inconsistent quality across documents, and inevitable burnout.
There's another way. The systematic approach acknowledges that 80% of documentation follows predictable patterns. Templates handle the recurring structures. Structured SME engagement gets information efficiently. Parallel reviews compress the approval timeline. Automated quality checks catch errors before they reach reviewers. The result: a 3-day average cycle time, consistent quality, and sustainable 40-hour weeks.
The difference between these approaches isn't talent or work ethic. It's engineering versus craftsmanship. Both produce documentation. Only one scales.
The Complete Workflow: Six Phases That Actually Work
After two years of iteration, I've distilled high-volume documentation into six distinct phases. Each phase has clear triggers, defined actions, and measurable outputs. The system works because it removes decision-making from the moment of execution and front-loads choices into process design.
Phase 1: Intake and Tracking
Every documentation request needs to enter your system the same way. No exceptions. The moment you start accepting requests through varied channels—a Slack DM here, an email there, a casual mention in a meeting—you've lost control of your backlog.
The trigger for documentation creation should be automatic. When engineering merges a feature to main, a Jira integration creates a doc ticket. When product launches a capability, same thing. When support identifies a customer-facing change that needs documentation, they have a standardised form that creates the ticket.
Each ticket auto-populates with essential information: feature name, engineering lead, target publish date, priority level. Crucially, the template is pre-selected based on feature type. API endpoint? That's the API template. UI feature? Different template. Integration? Another template. Conceptual explanation? Yet another.
My Jira board shows all documentation in pipeline: Backlog, In Progress, SME Review, Eng Review, Product Review, Published. At any moment, I can see exactly where every doc stands. No hunting through Slack threads. No wondering what I've forgotten. The board is the single source of truth.
Chaos handles deadline tracking across the pipeline. Every doc has a due date, and the system auto-reminds me two days before deadline. More importantly, it shows the pipeline view: what's coming up, what's blocked, what needs attention today.
Phase 2: Research and SME Interview
Getting information from subject matter experts is where most technical writers fail. Engineers are busy. They're shipping code, fixing bugs, attending meetings. Documentation is never their top priority, nor should it be. Your job is to extract maximum information in minimum time.
I use a structured interview framework capped at fifteen minutes. That's not a suggestion—it's a commitment I make to every SME. "I need fifteen minutes to document this feature. I've got five specific questions. Can we schedule that?"
For product features, my five questions are consistent: What problem does this solve for users? Walk me through the happy path step-by-step. What are the edge cases or limitations? What should users know before using this? What's most likely to confuse users? That last question is gold—it often reveals the crucial detail that makes the doc actually useful.
For API endpoints, different questions: What's the use case for this endpoint? Walk me through request and response. What are authentication requirements? What errors might users encounter? What's the rate limit or quota?
For integrations: What external service does this integrate? What's the setup process? What credentials or configuration needed? What's the data flow? What breaks this integration?
When live interviews aren't possible—and they often aren't—Loom provides an asynchronous alternative. I send a message: "Can you record a five-minute Loom walking through the feature? Specifically: these three questions. I'll draft the doc and share for your review." SMEs do it on their schedule. I can replay and pause—no reliance on my frantic in-meeting notes. And I have timestamp references for quotes.
The pre-interview email goes out 24 hours ahead: "I'm documenting this feature. I need fifteen minutes of your time. Here are my five questions. Can we meet at one of these three times? Or if async is easier, a Loom answering these would work." Clear, low-friction, respectful of their time.
Phase 3: Drafting with Templates
Templates are the single biggest time-saving innovation in my system. Without templates, drafting a typical doc takes three to four hours. With templates, it takes 45 to 90 minutes. That's a 60-70% time reduction, and it compounds across 50+ docs monthly.
My Confluence template library has four core templates covering the vast majority of documentation needs.
The UI Feature template covers product features with user interfaces. Structure: Overview in one sentence. Who is this for (the persona). How to access (navigation path). Step-by-step guide with numbered instructions and screenshots. Tips and best practices. Troubleshooting common issues. Related features with links.
The API Endpoint template handles REST API documentation. Structure: Endpoint summary. Authentication requirements. Request parameters in a table format. Response schema with tables and examples. Error codes in a table. Rate limits. Code examples in cURL, Python, and JavaScript. Changelog.
The Integration Guide template covers third-party integrations. Structure: Integration overview. Prerequisites. Setup steps numbered. Configuration options. Testing your integration. Troubleshooting. FAQ section.
The Conceptual Explanation template handles architectural and domain concepts. Structure: What is the concept? Why it matters. How it works with a diagram. When to use. Related concepts. Further reading.
Beyond these core structures, I maintain macros for common patterns: warning callouts, tip callouts, code blocks with syntax highlighting, screenshots with captions, related links panels. These macros ensure visual consistency across all documentation whilst speeding up formatting.
Screenshot guidelines matter more than most writers realise. All screenshots must be minimum 1600 pixels wide so they're readable when scaled. Relevant UI elements get highlighted with red boxes. Annotations use numbered callouts. Filename convention follows feature-name-step-number.png format. All images store in a consistent path: /images/docs/feature-name/. Consistency here prevents future maintenance headaches.
Phase 4: Review Orchestration with Parallel Processing
Here's where most documentation workflows waste enormous time. The serial review process goes like this: send to engineering, wait three days, incorporate feedback, send to product, wait two days, incorporate feedback, send to legal, wait two days. That's seven days minimum just in review time, assuming no one requests changes that restart the cycle.
Parallel reviews cut this to two days maximum.
I send to all reviewers simultaneously with a standardised email template:
Subject: REVIEW NEEDED - Feature Name Documentation
"Hi Engineering Lead, Product Lead, Legal. New doc for this feature is ready for review: Confluence link. Review SLAs: Engineering for technical accuracy, 48 hours. Product for messaging and positioning, 48 hours. Legal for compliance and claims, 48 hours. I'll incorporate all feedback and publish on target date. If you need changes, please comment directly in Confluence. If no comments by deadline, I'll assume approved."
That last line is crucial. Silence equals approval. This prevents indefinite blocking by reviewers who never get around to responding.
Review SLA enforcement follows a clear escalation path. Chaos sends reminders at 24 hours before deadline. If there's no response by 48 hours, I send a Slack ping. At 72 hours, I email the reviewer's manager with them copied, noting that I need their feedback by end of day to hit the publish date. At 96 hours with still no response, I publish with a disclaimer noting the doc is pending their review and may update.
When engineering and product disagree—and they will—I don't pick sides. The escalation goes back to them: "You've requested conflicting changes around technical accuracy versus user-friendly language. Can you align on approach? I'll implement whatever you agree on." This respects their expertise whilst keeping me out of political crossfire.
Phase 5: Publication and SEO
Before clicking publish, I run through a standardised publication checklist. All reviewer comments addressed. Screenshots added and captioned. Code examples tested—actually run the code, don't assume it works. Internal links to three to five related docs minimum. External links to authoritative sources where applicable. Meta description added in Confluence's SEO field. Labels and tags applied for discoverability. Table of contents if over 500 words. Mobile-readable—test on your phone. Added to navigation and site map.
SEO optimisation follows best practices: primary keyword in H1, keywords in first paragraph, H2 structure for scannability, alt text for all images, meta description under 155 characters.
Phase 6: Post-Publish Monitoring
Documentation isn't done when it's published. The first week after publish, I check Confluence analytics for page views and time on page. I monitor support tickets—are the docs actually answering questions? I solicit feedback in product and engineering Slack channels: "New docs for this feature, feedback welcome."
At month one, I update based on feedback, add FAQ sections if common questions emerged, and check whether the feature changed. Engineering often ships updates without telling anyone.
Quarterly, I audit all docs for accuracy, archive deprecated feature documentation, and update screenshots when the UI changes.
The Tool Stack That Makes It Work
Each tool in my stack serves a specific purpose. Jira handles feature tracking with custom doc ticket types auto-created when features ship. Fields capture feature name, engineering lead, target publish date, and priority. Cost is typically included in your company's existing Jira subscription.
Confluence serves as the documentation platform itself. My template library lives here with four core templates plus variations. Macros handle callouts, code blocks, and related links. Review commenting lets stakeholders comment inline. Version history allows rollback when needed. Again, typically included in the company Atlassian suite.
Loom handles asynchronous SME interviews. Engineers record screen and voice walkthroughs. Timestamps let me reference specific moments. Share links mean no downloads required. Cost runs about eight pounds per user monthly on the business plan.
Chaos tracks all documentation deadlines. Auto-reminders fire two days before deadline. The pipeline view shows what's next. Cost is roughly eight pounds monthly.
Grammarly Business catches typos and grammar before review—respecting reviewer time means not wasting it on basic errors. Tone suggestions help calibrate enterprise versus casual voice. Plagiarism checks ensure original explanations. Cost is about twelve pounds fifty per user monthly.
Total tools cost runs approximately thirty pounds monthly per writer, often company-provided. The ROI is substantial when you consider the alternative: missed deadlines, inconsistent quality, and burnt-out writers.
SME Interview Scripts That Actually Get Information
The challenge with subject matter expert interviews is that engineers are busy. You can't monopolise their time, but you need comprehensive information. The solution is structured efficiency.
Pre-interview, send an email 24 hours ahead: "I'm documenting this feature. I need fifteen minutes of your time to walk through it. I've drafted five questions below. Can we meet at one of these times? Or if async is easier, a five-minute Loom answering these would work."
During the interview, time-box strictly to fifteen minutes. Zero to two minutes: "Walk me through the feature at high level." Two to seven minutes: "Show me the happy path"—watch them actually use it. Seven to twelve minutes: specific questions from the framework appropriate to the feature type. Twelve to fifteen minutes: "What will confuse users most?" This last question often reveals the crucial detail that makes documentation actually useful.
Always record with consent: "I'm recording so I can focus on listening instead of frantic note-taking. Is that OK?" Everyone says yes. Now you have reference material.
Post-interview, email within two hours: "Thanks for walking me through the feature. I've drafted the doc here. Can you review by this date? Specific things to check: technical accuracy, did I miss any gotchas?" Quick turnaround shows respect for their time and keeps momentum.
Review Orchestration Across Multiple Stakeholders
Different reviewers care about different things. Engineering leads focus on technical accuracy—do the code examples work? Is the description of functionality correct? Product managers focus on messaging and positioning—is the user benefit clear? Is this aligned with how we talk about the product? Legal and compliance focus on claims and regulatory requirements—are there any statements that could create liability?
The stakeholder matrix clarifies who blocks publication and who merely provides feedback. Engineering is typically a hard blocker—you can't publish if the documentation is technically wrong. Product is usually a soft blocker—nice to have their input, but not strictly required. Legal depends on your industry; in regulated sectors, they're a hard blocker. Support provides feedback but doesn't block.
SLA enforcement matters. At 48 hours without response, send a Slack ping. At 72 hours, email the reviewer's manager with them copied, noting you need feedback by end of day to hit your publish date. At 96 hours, publish with a disclaimer noting the doc is pending review and may update. This prevents infinite blocking whilst maintaining documentation velocity.
Quality Gates: Automated Where Possible
Quality gates catch issues before they reach reviewers. Gate one: spelling and grammar via Grammarly scan. Target zero errors reaching reviewers—respect their time. Gate two: link validation using Confluence's built-in checker, ensuring all internal links resolve and external links point to stable resources. Gate three: code example testing—actually run every code snippet, verify syntax is correct. Typos in code undermine credibility. Gate four: screenshot audit ensuring all screenshots match current UI, include annotations where necessary, have alt text for accessibility, and render readable on mobile. Gate five: template compliance checking whether the doc matches its template structure with all required sections present. Gate six: SEO check verifying primary keyword in H1 and first paragraph, proper H2 structure, written meta description, and image alt text including keywords.
Confluence plugins can automate several of these checks: broken links, missing alt text, orphaned pages. Invest time in setting up automated quality checks—they pay dividends across hundreds of documents.
Time-Saving Automations Worth Building
Four automations save me approximately twenty hours monthly.
Automation one: doc ticket auto-creation. A Jira automation triggers when a feature ticket moves to Done. It creates a documentation ticket automatically, populates feature name, engineering lead, and target date, and assigns it to me. Time saved: roughly thirty minutes weekly chasing feature completion status.
Automation two: review reminder cascade. When a doc enters Review state in Chaos, the integration auto-sends review request emails to all stakeholders, auto-reminds at 24 hours before SLA, and auto-escalates if SLA is missed. Time saved: approximately two hours weekly on manual reminder chasing.
Automation three: template auto-population. A Confluence macro triggers when creating a new doc from template. It auto-fills feature name, author, date, and placeholder content. Time saved: five minutes per doc times fifty docs equals four hours monthly.
Automation four: screenshot workflow. Using Keyboard Maestro on Mac or AutoHotkey on Windows, a keyboard shortcut takes a screenshot, auto-renames it following the convention, uploads to Confluence, and inserts at cursor. Time saved: ten minutes per doc times fifty docs equals over eight hours monthly.
Metrics That Actually Matter
Volume metrics track output: docs published monthly with a target of 40 to 60, average cycle time from intake to published with a target under four days, and backlog size with a target under twenty items.
Quality metrics track excellence: review iteration count with a target under two rounds, post-publish updates needed with a target under ten percent, and support ticket reduction for documented features measuring actual impact.
Engagement metrics track usefulness: page views indicating discoverability, time on page indicating readability, and feedback ratings showing user satisfaction.
Productivity metrics track sustainability: docs per week measuring personal capacity around twelve to fifteen, time per doc type identifying optimisation opportunities, and review SLA compliance targeting above ninety percent.
Monthly dashboard reviews identify trends: bottlenecks where docs slow down, quality issues with high revision rates, and engagement gaps with low views. Act on these trends to continuously improve the system.
Handling Difficult Situations
Situation one: engineering shipped without telling you. Response: post in the engineering Slack channel noting you see the feature shipped to prod, you're creating docs now, and ask the engineering lead to confirm scope and known issues. Be reactive but professional. Document that this happened—if it becomes a pattern, raise it in retrospective.
Situation two: SME won't make time for interview. Escalation path: first email offering fifteen minutes or async Loom. If no response in 48 hours, email their manager asking for help prioritising. If still no response, document from code and release notes with a disclaimer noting it's pending SME review.
Situation three: reviewers disagree. Don't pick sides. Escalate to both with a message noting they've requested conflicting changes and asking them to align on approach. You'll implement whatever they agree on.
Situation four: feature changed after documentation. This happens constantly. Monitoring catches it through support tickets or engineering mentions. Quick update within 24 hours. If frequent, raise with engineering asking whether you can sync docs and deploys.
Situation five: overwhelming backlog. Triage ruthlessly. P0: customer-facing, revenue-impacting—do first. P1: frequently asked, support burden—do next. P2: nice-to-have, low usage—backlog. P3: internal tooling, low priority—skip unless specifically requested. Communicate the backlog to stakeholders: "Current capacity is twelve docs weekly. Here's the priority order. Disagree? Let's discuss."
Scaling Beyond Your Capacity
If you hit capacity ceiling despite optimisation, four options exist.
Option one: hire another writer. Share templates and processes. Divide by product area or doc type. The system you've built makes onboarding dramatically faster.
Option two: enable self-service. Train product managers to draft docs that you edit. Provide templates plus training. You become editor rather than author, dramatically increasing throughput.
Option three: automate further. AI draft generation from Jira tickets is experimental but promising. It still requires human review and editing, but initial drafts accelerate production.
Option four: ruthless prioritisation. Not everything needs comprehensive documentation. Some features warrant release notes only. Focus depth on high-impact features where documentation measurably affects user success.
The Key Takeaways
Managing fifty-plus docs monthly requires systematic workflow: tracking to SME interview to templated drafting to parallel reviews to publication to monitoring. Following this system reduced my cycle time from eight days to three days whilst improving quality and eliminating overtime.
The core tools—Jira for tracking, Confluence for templates and macros, Loom for async SME interviews, Chaos for deadlines, Grammarly for quality—cost roughly thirty pounds monthly and save hours weekly.
Template library covers eighty percent of documentation: UI features, API endpoints, integrations, and conceptual explanations in four core templates.
Parallel reviews instead of serial reviews save five days: send to engineering, product, and legal simultaneously with clear SLAs and automatic escalation.
Automation saves twenty hours monthly across ticket creation, review reminders, template population, and screenshot workflow.
If you're drowning in documentation backlog, the answer isn't working harder. It's building systems that work smarter. The investment in process design pays dividends across every subsequent document.
Chaos tracks all documentation deadlines and auto-reminds stakeholders at review SLAs—keeping high-volume documentation on schedule. The systematic approach transforms technical writing from crisis management into sustainable production. And that transformation starts with treating documentation as engineering problem, not creative endeavour.