Ever found yourself staring at a half-finished technical spec at 2 AM, desperately trying to polish it before the morning stand-up? Or perhaps you’ve been on the receiving end, slogging through someone else’s 47-page opus, wondering if unlimited PTO is worth it after all. We’ve all been there. It’s easy to dismiss technical specifications as bureaucratic busywork, another hurdle between an idea and actually shipping code.
But here’s the thing: when done well, technical specifications aren’t homework from your tech lead. They are one of the most powerful tools you have to drive features from concept to production. They’re how ideas become reality, efficiently and effectively. Having worked in software engineering at companies like Monzo and picking up lessons from engineers at AWS, Google, and Meta, I’ve refined an approach that makes specs a genuine asset. While my background is in software, much of this philosophy applies across technical disciplines. Let’s dive in.
The Silent Scourge of Software Development (And Its Secret Weapon)
I get it. Writing documentation feels like a distraction when you could be shipping code. But stick with me, because a good technical spec does far more than satisfy a process requirement. It’s an investment that pays dividends, preventing countless headaches down the line.
When used effectively, technical specs help you validate an idea before you waste weeks building it. I’ve personally killed more bad ideas during the spec phase than I care to admit. The act of writing forces you to meticulously think through edge cases you’d otherwise discover three sprints deep, when changes are far more costly. They’re also your secret weapon for gaining buy-in from key stakeholders – your staff engineer, your product manager, that principal engineer who’s seen it all. Getting their thumbs up early means you won’t be defending your approach in a contentious PR review later, when emotions are high and time is scarce.
A well-crafted spec is also a fantastic way to tap into your team’s collective intelligence. I’m not the smartest person in the room, and if you’re honest, neither are you. A spec is how you open up your plan to smarter people who can poke holes, suggest improvements, and ultimately make your solution more robust. It streamlines knowledge sharing, turning new engineer onboarding from “let me explain this architecture for the third time” into “read this spec.” Even beyond engineering, it bridges the gap, allowing product managers, compliance teams, or finance folks to understand what you’re building and why.
And here’s where things get even more interesting: LLMs. These new tools thrive on strict, air-gapped technical specifications. The less you leave to imagination, the better. A well-defined spec can be fed into an LLM for automatic ticket creation, test generation, or even to ideate on parts of your proposal and identify logical flaws. While I have another article coming soon dedicated to spec-driven development with LLMs, the discipline of writing a rigorous spec pays off regardless. It forces clarity. And clarity is the bedrock of good software.
Part 1: The “Why” Before the “What” – Building Consensus
A truly effective technical spec is well-structured, easy to understand, and actionable. It needs to be skimmable for executives and detailed enough for the engineer implementing it. It’s a narrow tightrope, but a pattern works. Here’s the first half of that pattern—what you nail down before seeking broad buy-in.
What Problem Are You Actually Solving?
This is where many go wrong, jumping straight to “we should build a microservice with GraphQL” without ever explaining what’s broken. Start with the problem statement, not the solution. It should be clear enough that someone outside your team can understand it. At my startup, Tandem, I use a simple test: could Jack, my non-technical co-founder, grasp the problem just by reading this? If not, it needs a rewrite.
Use concrete examples: not “users experience latency” but “users wait an average of 8 seconds for their transaction history to load, leading to a 23% drop-off.” Quantify the business impact – “This costs us approximately £2.3M annually in lost conversions” is far more compelling than “this is slow.” Include data, user research, or customer complaints to back up your claims; evidence beats opinion every time.
What Are You *Not* Trying to Solve?
This section is criminally underused and incredibly valuable. Scope creep is real. If you don’t explicitly call out what’s out of scope, you’ll spend the next month arguing about edge cases that don’t matter. I once saw a “simple UI library upgrade” balloon into a six-month migration effort because the routing system changes and navigation patterns were never explicitly out of scope. An explicit “what we’re NOT solving” section gives you permission to say no, keeps “why-notism” at bay, and protects your timeline from innocent-sounding suggestions that become organizational nightmares.
Why Now? And What are the Non-Negotiables?
Not all problems need solving, or at least, not right now. Make a clear case for why this particular problem deserves engineering time. What’s the impact on users, the business, engineering, or compliance? Show your work. We once justified a button text change with an experiment proving a 23% uptick in conversion, translating to +£12k ARR. Numbers speak louder than “this could be better.”
Timing also matters. Why is now the optimal time? Consider external deadlines, internal readiness, dependencies, or narrow opportunity windows. For instance, we proposed using local time zones on bank account statements right after daylight savings ended in the UK. This gave us six months where UTC and local time were the same, greatly simplifying the migration and avoiding complex edge cases. If we’d done it in March, it would have been a nightmare.
Finally, list your requirements – the absolute must-haves. These should be testable statements like “Transaction history must load in under 2 seconds at p95” or “Solution must work for users on iOS 14 and above.” Vague requirements like “must be fast” are useless; pin them down.
The Bird’s Eye View: Solution at 30,000 Feet
Here’s where most people jump into implementation details. Don’t. This section should be understandable by non-technical stakeholders – product managers, operations, even finance. If you can’t explain your solution to someone who doesn’t know Kubernetes, you don’t understand it well enough yourself. No tech talk yet. Instead of “we’ll deploy a new microservice with a Redis cache,” say “we’ll store frequently-accessed data closer to the user so it loads instantly.” Focus on user outcomes, anchor on what success looks like for them, and if possible, offer multiple potential solutions with pros and cons. This demonstrates thorough thought and offers real choice.
Before you proceed, pause and assess: Is your problem statement clear and specific? Is the business impact quantified? Are non-goals explicitly stated? Are requirements testable? Does the high-level solution make sense to a non-engineer? Are you confident this is worth doing? If you can’t check all these boxes, don’t move to Part 2 yet. Seriously.
Part 2: From Blueprint to Production – Detailing the “How”
The best technical specifications are a result of strong collaboration. You write the first draft, then invite smart people to make it better. The spec evolves.
Best Practices, Prior Art, and the Technical Approach
Your implementation plan should be an extension of your company’s established practices. Only propose changes with strong evidence: outside proof-of-concept, prior art from adjacent domains, clear reasoning for why the current approach fails, and a detailed migration path. At Superbet, I led a team building a dedicated UI library, technically perfect, but failed to convince most teams because I couldn’t drive the business case home. Technical excellence alone doesn’t win the argument; numbers and a clear path do.
Always consider prior art. I’ve saved months of engineering time by finding existing solutions or implementation patterns. What other approaches did you evaluate? Why didn’t they make the cut? A simple table of pros, cons, and rejection reasons prevents others from suggesting paths you’ve already dismissed.
Now, finally, dive into the technical details. Use diagrams – architecture, sequence, data flow. Tools like Draw.io or Mermaid (for markdown-based sequence diagrams) are excellent. Explain the “why” behind decisions, not just the “what.” Don’t just say “we’ll use Redis for caching”; explain *why* Redis for your specific read-biased access patterns and needed data structures. Call out performance characteristics and scalability limits honestly. Mention data models, API contracts (with JSON examples), and any new infrastructure needed. The more detail and clarity here, the less ambiguity later.
Breaking Down the Work and Defining Success
Decompose the solution into trackable milestones and tasks. Split into phases that deliver incremental value: MVP → MLP → Full Product. Often, the MLP is “good enough.” Identify critical path items versus nice-to-haves (MoSCoW is great here). Make dependencies explicit, and assign rough estimates (developer-hours, -days, -weeks, padding them generously—I typically multiply by 1.5x to 2x for unfamiliar territory).
How will you measure success? “Make it faster” is not a metric. “Reduce p95 API latency from 500ms to under 200ms” is. Define measurable outcomes for both business and technical metrics. Set baseline metrics before implementation, often with screenshots of current dashboards. Specify how you’ll track these: a new dashboard? Alerts? A/B tests?
Risks, Rollouts, and Open Questions
Be brutally honest in your risk assessment. Pretending risks don’t exist doesn’t make them go away; it just means you’ll have no plan when they materialize. List external dependencies, single points of failure, and areas of high uncertainty. For each major risk, propose a mitigation strategy or fallback plan. Example: “Risk: Third-party API downtime. Mitigation: Circuit breaker with exponential backoff; cache responses; degraded mode.” Be transparent about what you don’t know yet, and mention compliance or security concerns early.
Your rollout strategy should never be “ship to 100% on day one.” Plan gradual rollouts (e.g., 5% → 10% → 50% → 100%) using feature flags to decouple deploy from release. Define specific rollback criteria (e.g., “If error rate exceeds 0.5%, roll back”). Consider dark launching (shadow testing) and separate data migrations from code deploys. Have a communication plan for internal and external stakeholders.
Finally, address your testing approach. Define test coverage expectations, plan load/performance testing, consider chaos engineering, and explicitly test edge cases and failure modes. Don’t forget manual QA and security testing for sensitive features.
Use an “Open Questions” section that shrinks over time. List unknowns that need research, prototyping, or specific input from other teams. Assign owners and target resolution dates. This shows intellectual honesty and invites collaboration.
The Spec *Is* The Work: Cultivating a Culture of Clarity
I’ve worked at companies where specs were bureaucratic box-checking and where they were genuinely valued. The difference is night and day. Adopting a standard template (like the one I share on GitHub) reduces cognitive load and makes reviews easier. Make all proposals publicly available; transparency leads to serendipity and invaluable cross-team feedback. Schedule regular review sessions for critical proposals to encourage collaboration and organizational awareness. And critically, praise great proposals publicly. Acknowledge contributors. This fosters a culture where input is valued, even critical feedback.
Here’s my final, and perhaps most important, thought: writing the spec IS doing the work. Too many engineers treat it as overhead before the “real work” of coding begins. That’s backwards. The spec is where you do the hardest thinking, where you discover what you don’t know, and where you find fatal flaws before they become production incidents. The best specs I’ve written have been collaborative, iterative, and occasionally contentious. They’ve gone through multiple revisions, forcing me to reconsider assumptions I didn’t even know I was making. And every single one made the implementation smoother, faster, and ultimately more successful than it would have been otherwise.
So the next time you’re tempted to skip the spec and just start coding, resist. Open up that document. Start with the problem. Work through the details. Invite smart people to poke holes in your logic. Iterate. Your future self – and your on-call rotation – will thank you.
My product proposal template, which captures most of the ideas discussed here, is available on GitHub. If your organization doesn’t have a technical proposal template yet, use this as a starting point.
