Documenting Engineering Design Concepts

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.

𝗥𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗦𝗰𝗮𝗳𝗳𝗼𝗹𝗱𝗶𝗻𝗴 Your change review board approved a design change six months ago. Today, a similar problem has surfaced in a different product line. Nobody remembers why the original solution was chosen over two viable alternatives. The decision was documented. The rationale was not. This is not a documentation problem. It is a knowledge architecture problem. Research on design rationale has shown for decades that capturing the “why” behind decisions is critical for reuse and learning. Yet it rarely happens, because documenting rationale is intrusive, time-consuming, and disconnected from how engineers actually work. The real cost is compounding. New hires spend roughly 200 hours working inefficiently because contextual knowledge was never captured. Boeing’s experience in the 2010s demonstrated what happens when institutional memory erodes: integration failures on the 787 traced back to process knowledge that existed in people’s heads but never made it into retrievable records. This is where AI scaffolding changes the equation. Not AI generating rationale after the fact. AI captures it as a byproduct of the work itself. In an AI-assisted impact analysis, the engineer and the AI walk through a product structure together, level by level. At each step, the AI surfaces a dependency and explains why it matters. The engineer confirms, rejects, or redirects. That interactive exchange is itself the rationale record. The “why” is captured not because someone stopped to write it down, but because the process required articulating it. CM2’s closed-loop change process provides the structure that makes this work. The change objects, the baselines, the impact matrices, these are not just governance artifacts. They are the scaffolding that gives AI a consistent framework to interact with. Without that structure, you get a conversation. With it, you get a traceable decision record. The INCOSE Systems Engineering Handbook states that decisions should be documented using digital engineering artifacts, including the analysis, decisions, and rationale for historical traceability and future decisions. CM2 operationalizes this by defining exactly where in the change process those decisions live and who owns them. The uncomfortable question: if your organization cannot reconstruct why a decision was made six months ago, what makes you confident the next decision will be any better informed? #ConfigurationManagement #CM2 #KnowledgeManagement #ChangeManagement #DigitalEngineering #AI #PLM #CM #HowDoYOUCM2 #ProductLifecycleManagement #ProductMemory

The problem with keeping code and documentation separate. It tends to diverge over time. So most software documentation should be replaced with highly readable code and tests. However, no matter how readable the code is, it doesn’t give us the context behind why the code exists in the form that it does. To address this software engineering teams should be keeping Architecture Decision Records (ADRs). These are lightweight documents that capture important architectural decisions along with the when, how and why of the decision. ADRs are particularly useful for agile projects where not all the decisions will be made at once and long lifetime projects where a new person joining the team, or someone who hasn’t worked on part of the project recently, will need to understand the rationale and consequences of a decision that has been made in the past. An ADR should record: 1. When the decision was made. 2. The status of the ADR (proposed, accepted, rejected, etc.). 3. The current context and business priorities that relate to the decision. 4. The decision and what will be done because of the decision. 5. The consequences of making the architectural decision. ADRs are best stored in the revision control system alongside the code that they relate to, keeping the context with the code.

Early in my career, I thought deep focus was enough. I’d disappear into my code for days and then resurface with a finished feature. It felt productive. It wasn’t. Once, I spent two weeks building something I was proud of, only to realize I had optimized for a direction that wasn’t aligned with the team’s thinking. Two weeks gone. That experience changed how I work. Now I try to, • Check in early • Share rough designs • Ask clarifying questions • Align before I build One practice I really learned to value at Google is writing design docs. Before implementation, we write down: – The problem – The context – The proposed approach – Trade-offs – Open questions It forces clarity. And often, it exposes gaps in thinking before a single line of code is written. Even if your company doesn’t have a formal design doc process, sending a simple one-pager before building can dramatically reduce rework. Technical skill builds features. Clear communication builds the right features. What’s one habit that made you more effective as an engineer?

Why do we still need design doc? In a world where AI generates code in seconds, design doc is actually more important than ever but its purpose has changed profoundly. When an engineer hands me a design doc today, I’m looking for three things: the What, the Why, and the Who. But there’s a new, aggressive advantage to writing docs in the AI era: The death of guesswork. 1. The WHAT: Solving the Right Problem AI is dangerously good at solving the wrong problem perfectly. As a manager, I need to see that the engineer has identified the core business constraint. The doc proves they have the right problem to set the boundaries before the AI starts typing. 2. The WHY: Validated by Data, Not Opinions In the past, we debated technical choices for weeks because building "Plan B" was too expensive. Not anymore. Today, the How is so cheap that an engineer can use AI to prototype three different architectural paths in a single afternoon. • The design doc now reflects this: "We tested a Graph DB, a Relational DB, and a Key-Value store. Here is the latency data for all three." • I’m looking for the engineer who uses AI to replace "I think" with "The data shows." 3. The WHO: The Person Who Connects the Dots This is the "Proof of Person." Through the quality of the writing, I identify the right owner for the project. • The "Who" is the person who can articulate the What and Why so clearly that the AI execution becomes a formality. • High-quality writing is the ultimate signal for high-quality thinking. If you can simplify a complex trade-off on paper, I know you can manage the complexity of the system. I don’t care how fast an AI can write your code. I care how fast you can convince me that you understand the problem and have validated the best solution. In 2026, we don't write docs because code is hard to write. We write them to prove we are solving the right problem, that we’ve used AI to explore every "How," settled on "Why," and that you are the "Who" capable of leading the charge. 🏆 #EngineeringManagement #TechLeadership #SoftwareArchitecture #AIRevolution #DataDriven #SystemDesign

When projects or companies scale, senior engineers are often the ones who carry a substantial historical context. When this context is shared and documented (whether as internal comments next to the code, or diagrams explaining the flow, or a simple document), they can spend less time re-explaining the same concepts and more time actually delivering value. That shift has a measurable impact on business. With easily accessible project knowledge, onboarding cycles shorten, and estimation accuracy improves because decisions are easy to reference. Architectural debates focus on the future direction rather than on decoding previous reasoning. Structured knowledge ensures that capacity translates into output, rather than internal dependency. Over time, that consistency strengthens forecasting, improves delivery rhythm, and protects margins as project complexity increases.

In the past 10 years, I’ve reviewed 100s of design docs. Here’s how to write review-ready design docs in 3 simple steps. 1/ Start with a skeleton, write these: • Metadata (Title, authors, status, date, reviewers, approvers) • Context and background • Problem statement • Summary or tl;dr (Optional) • Proposed solution details with tradeoffs and selection rationale  • Other alternatives considered • Failure modes of the proposed solution • Open Questions • References (Optional) 2/ After the skeleton, fill in the content under these headings. -If there are sub-sections, add sub-headings.  -Provide examples and sample calculations. -Use bullet points and lists wherever applicable -Include architectural diagrams, graphs and tables. 3/ If the document is large, put a summary after the problem statement. Start with the skeleton, take it one step at a time, and before you know it, you are done! Remember, a good design doc: -helps understand design decisions and implementation details -helps in identifying potential issues and challenges early  -gives a clear understanding of the architecture -serves as a reference doc during the project While you write and review, make sure your work follows these guidelines. I know writing detailed docs doesn’t come naturally when you’re focused on problem solving. But it’s an essential skill you have to learn to level up. just follow a simple procedure, practice and you’ll get the hang of it. – P.S: Check out additional writing tips in the comments below ↓