Streamlining Technical Writing For Engineering Teams

𝗠𝗼𝘀𝘁 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗶𝘀 𝗯𝗹𝗼𝗮𝘁𝗲𝗱 Engineers use three words where one works. Managers hide behind passive voice. Everyone picks complicated words to sound authoritative. Orwell saw this in 1946. His 𝘀𝗶𝘅 𝗿𝘂𝗹𝗲𝘀 still work: 𝟭. 𝗡𝗲𝘃𝗲𝗿 𝘂𝘀𝗲 𝘁𝗶𝗿𝗲𝗱 𝗺𝗲𝘁𝗮𝗽𝗵𝗼𝗿𝘀. If you've read it a hundred times, skip it. "Move the needle" and "low-hanging fruit" mean nothing now. 𝟮. 𝗨𝘀𝗲 𝘀𝗵𝗼𝗿𝘁 𝘄𝗼𝗿𝗱𝘀. "Use" beats "utilize." "End" beats "terminate." Short words are faster to read and harder to misunderstand. 𝟯. 𝗖𝘂𝘁 𝗿𝗲𝗹𝗲𝗻𝘁𝗹𝗲𝘀𝘀𝗹𝘆. If removing a word doesn't change meaning, remove it. Most first drafts carry 30% filler. 𝟰. 𝗖𝗵𝗼𝗼𝘀𝗲 𝗮𝗰𝘁𝗶𝘃𝗲 𝘃𝗼𝗶𝗰𝗲. "We deployed the service" is stronger than "The service was deployed." Active voice shows who did what. 𝟱. 𝗗𝗿𝗼𝗽 𝘁𝗵𝗲 𝗷𝗮𝗿𝗴𝗼𝗻. Unless you're writing for specialists in your exact domain, use everyday words. "Fix" works better than "remediate." 𝟲. 𝗕𝗿𝗲𝗮𝗸 𝘁𝗵𝗲 𝗿𝘂𝗹𝗲𝘀 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂 𝘀𝗼𝘂𝗻𝗱 𝗿𝗶𝗱𝗶𝗰𝘂𝗹𝗼𝘂𝘀. Sometimes passive voice works. Sometimes you need technical precision. The goal is clarity, not dogma. Before you ship any writing, ask Orwell's 𝘀𝗶𝘅 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: - What am I trying to say? - What words express it? - What image makes it clearer? - Is this image fresh? - Could I say it shorter? - Have I written anything ugly? These questions force you to think. Most bad writing comes from not thinking hard enough about what you're communicating These rules work everywhere: PR documents, architecture docs, incident reports, code reviews, team updates My workflow: Draft fast → cut 20% → swap passive to active → replace jargon → read aloud once Clear writing gets things done faster with less confusion

The difference between a good design doc and a great one is usually clarity. Technical writing should be crisp and to the point. So, it is always better to treat every sentence like it has a cost. After writing, cut aggressively. Remove extra words. Then check if a line can go. Sometimes even a full paragraph is unnecessary. One thing I always do is to start the doc with the conclusion; this way, the reader/reviewer knows where we are heading. This is contrary to how most engineers write docs - listing every approach first and only concluding at the end. That slows readers down. I avoid this because long explanations make people lose track; most readers want the conclusion quickly. So, always start with the answer and why it matters. Then add details and alternatives below for those who want depth. A habit that helps is a quick editing pass like this: - Remove filler words and repeated ideas. - Break long sentences into smaller ones. - Prefer bullets when listing options or steps. - Check if the first section clearly states the outcome. - Add a link or short explanation where a reader may pause. Empathy matters more than most people realize. Try to read your document as someone new to the topic. Ask yourself what might confuse them. Add the missing context. Add the helpful link. Let the ideas evolve naturally from problem to solution. This skill develops over time. Use simple language and fewer buzzwords. The goal is to communicate, not impress. Simple documents get read more. More readers means better alignment and better visibility for the work. Finally, always provide enough context. A short setup about the problem, constraints, and prior decisions goes a long way. It helps readers understand why the decision exists, and, of course, it prevents unnecessary back and forth later. Hope this helps.

Firms waste money when they hire technical writers and then use them like formatting support. Yes, formatting matters. Grammar matters. Professional presentation matters. But that is not the strategic value of a technical writer. A good technical writer is looking for the place where the reader is going to misunderstand the instruction, skip the warning, choose the wrong form, miss the deadline, abandon the process, or call three people for an answer that should have been in the document. That is not cleanup work. It is risk reduction. Aviation and aerospace figured this out a long time ago. Those industries became so concerned about misunderstanding, maintenance errors, and translation problems that they developed controlled technical language standards for documentation. One of them—ASD-STE100 Simplified Technical English—restricts vocabulary, sentence structure, and ambiguity in technical instructions. Not because pilots, mechanics, or engineers are incapable of understanding complexity. Because people under pressure misread things. They skip steps. They interpret wording differently. They work while tired, distracted, overloaded, or translating information across languages. At some point, the industry realized unclear writing was not just an inconvenience. It was operational risk. That is the part many organizations still miss. A procedure is not successful because it exists. It is successful when someone can use it correctly at the moment it matters. In healthcare, unclear discharge or medication instructions can send someone home with the wrong understanding of what to do next. In accessibility work, I see this constantly. A form can be “available” and still unusable. Instructions can be present and still fail. A PDF can look finished while the tag structure makes it miserable—or impossible—for someone using assistive technology. None of that is just formatting. It is what happens when information was published before anyone asked, “Can a real person complete the task with this?” Technical writers ask that question. They test the logic. They find the missing context. They catch the buried assumption. They notice when the structure does not match the way the work actually happens. If your technical writer spends all day fixing fonts, spacing, and commas, your organization is wasting a strategic role. Because good technical writing is not cleanup after the work. It is part of how the work becomes usable. Caption: Good documentation assumes humans will be human.

Klaus has been with the company for 31 years. He designed the original control system. He knows why every design decision was made. He knows which tolerances matter and which ones are just legacy numbers nobody questioned. Klaus retires in April. When I asked the engineering manager what they’d done to capture Klaus’s knowledge, she said: “He’s been meaning to write some things down.” “Some things.” 31 years of institutional knowledge. “Some things.” This is the most expensive knowledge loss in manufacturing, and it happens every single quarter at companies across Germany. The documentation team’s role isn’t just writing manuals. It’s being the institutional memory of the organization. Here’s the framework we use for proactive knowledge capture: → Structured interviews (not “tell me everything”—specific questions about specific decisions) → Decision logs (why was this designed this way? what alternatives were considered?) → Process documentation (not just what to do, but what to watch for and why) → Video walkthroughs of complex procedures (30-minute sessions, not Hollywood productions) → Validation sessions (have the departing expert review what was captured) Start 6 months before the retirement date. Not 6 weeks. The knowledge walking out the door is worth more than the pension walking in. #KnowledgeManagement #TechnicalWriting #Manufacturing #Documentation

In my different leadership roles, I’ve always struggled to get developers to write and maintain relevant documentation. It’s been their bête noire. And when they do write it, it’s usually under managerial pressure, resulting in long SharePoint or Confluence pages that slowly rot, disconnected from the actual architecture and code. In my last mission, I decided to face the elephant in the room and change the approach. Surprisingly, it worked. (Partly thanks to LLMs 😊) Here’s what we did: We moved to collaborative documentation (Microsoft Loop — but it could be Notion, Confluence, whatever fits your team). I encouraged devs to use ChatGPT — not to replace them, but to help them structure, improve, and enrich their writing. We made architecture and design discussions async and doc-first. I introduced a simple rule: 👉 “If it’s not on Loop, it doesn’t exist.” Within a few months, we went from static, outdated SharePoint pages... ...to living docs and Architecture Decision Records (ADRs) — that became a shared knowledge base for the team and the broader company. Of course, AI tools won’t write your docs for you. But they can help your team build a real documentation culture. And you — what’s your experience with documentation? #softwareengineering #engineeringmanagement #documentation #softwaredocumentation #architecture #adr #softwarearchitecture

The closer technical writers are integrated with engineering teams, the better the documentation becomes. When writers are treated as essential members of the development process rather than an afterthought, they can create more accurate, timely, and valuable documentation that truly serves both users and developers. Docs-as-code is one of the most effective ways to achieve this integration. By using the same tools and workflows as developers, technical writers can participate directly in the software development lifecycle, catch documentation needs earlier, and maintain docs with the same rigor as code. For teams looking to get started with docs-as-code, here are some excellent tools to consider: Static Site Generators: - Jekyll (particularly with GitHub Pages) - MkDocs - Hugo - Sphinx (especially for Python projects) Documentation Linters: - Vale for style/grammar checking - markdownlint for Markdown consistency - awesome-lint for README files Version Control & Collaboration: - GitHub/GitLab for repository management - Pull request workflows for doc reviews - GitHub Actions for automated doc testing - LEARN GIT VIA COMMAND LINE The investment in learning these tools pays off quickly in improved #documentation quality, better collaboration with developers, and more efficient workflows. Most of these tools are open-source and well-documented, making them perfect for teams just starting their docs-as-code journey. #TechnicalWriting #DocsAsCode #Documentation #TechComm #DeveloperDocs #TechnicalCommunication

If you run an IT agency, you know how quickly the “busy work” piles up - writing documentation, summarizing tickets, preparing reports, updating clients, and organizing internal notes. These tasks are important, but they take up hours that should be spent on delivery, strategy, and scaling your business. That’s why I created this list of 20 powerful ChatGPT prompts designed to automate the repetitive work, streamline your workflows, and help your team save several hours every single week. 1. Daily Standup Summary “Summarize our team’s daily updates into a clear report highlighting key accomplishments, blockers, and tasks to prioritize tomorrow.” 2. Client Meeting Notes Organizer “Convert these raw meeting notes into a professional summary with key discussion points, client feedback, and next steps.” 3. Ticket Summary Helper “Summarize this long client support ticket into a short paragraph outlining the issue, resolution, and assigned handler.” 4. Weekly Progress Report Writer “Create a weekly report summarizing completed projects, system uptime, incidents resolved, and pending high-priority tasks.” 5. Client Onboarding Checklist Creator “Write a detailed client onboarding checklist including system access setup, credentials, documentation sharing, and communication steps.” 6. Standard Operating Procedure (SOP) Generator “Convert this process into a well-structured SOP with headers, numbered steps, and internal best practices.” 7. Knowledge Base Article Builder “Turn these repeated client questions into concise, easy-to-follow knowledge base articles.” 8. Incident Report Template “Write a professional incident report describing root cause, technical impact, timeline, and preventive measures.” 9. Internal Team Update “Draft a short internal email summarizing today’s updates, urgent issues, and what the team should focus on next.” 10. Technical Summary Simplifier “Rewrite this complex technical explanation into a client-friendly summary without jargon.” 11. Root Cause Analysis Summary “Summarize this system issue into an RCA report with the problem statement, cause, fix, and prevention recommendations.” 12. Client Progress Email Generator “Write a client update email summarizing key progress, improvements, and next steps.” 13. Documentation Outline Builder “Create a detailed outline for a new IT project including objectives, architecture, APIs, and testing.” 14. Task Breakdown Assistant “Convert this high-level project goal into smaller actionable tasks with time estimates and responsible team members.” 15. Employee Review Draft “Write a quarterly performance review focusing on productivity, collaboration, and problem-solving.” [Explore More In The Post] Want to increase your agency’s profit margin by 13% and boost productivity by 30%+? DM me to learn how you can automate your agency, train your team in AI, and scale smarter. Follow Denis Panjuta for more AI Automation Insights!

A 60 page doc tripled this engineering team's velocity, I've been talking to engineering leaders for weeks, and there's a pattern nobody's discussing yet: teams aren't optimising for better code anymore. They're optimising for better documentation. They went from "we barely document anything" to maintaining a 60-page AGENTS.md file in just 3 months. Not user docs. Not API specs. Business logic documentation specifically for AI agents. These files include coding conventions, business logic, good vs bad examples, testing workflows, and architecture reasoning. What changed? They started using Cursor and Claude Code heavily. They realized something: when AI writes your code, the quality of what it produces correlates DIRECTLY with how well you explain your business logic. The shift is fundamental: Old way: Write code, document later (maybe) New way: Document first, let AI write the code Another team I spoke with now tracks "context quality" as a KPI. They're not measuring lines of code anymore , they're measuring how well their documentation helps AI understand what they're building. The bottleneck isn't coding speed anymore. It's context clarity. For engineering leaders using AI tools: are you seeing this pattern? How are you handling documentation? PS: if you’re a CTO/Engineering leader facing testing bottleneck and interested in shipping 10x faster, let's chat - https://lnkd.in/gJJUET-S

Ever hit a wall of text and immediately tune out? That’s what happens when writing lacks structure. Good documentation isn’t just about what you say— it’s about how you present it. Here’s what makes content instantly clearer: ✅ Chunking – Break information into small, logical sections. One idea per chunk. ✅ Lists – Highlight key details instantly. Ordered for steps, unordered for related ideas. ✅ Headings – Act as street signs. Keep them short, clear, and easy to scan. When all three work together, the reader doesn’t have to think—they just find what they need. Before you publish, ask yourself: Can someone scan this and get the key points in seconds? If not, it’s time to restructure. Follow Andrew Eroh for Technical Writing Insights   #TechnicalWriting #TechComm #Documentation #UserExperience #ClearCommunication #WritingTips #TaskBasedWriting #ProcessImprovement #WritingProcess #EngineeringDocs