How to do prompt engineering right: A 7-Layer Framework

One of our engineers recently spent two weeks investigating a tricky performance issue. At the end of it all, the solution was a single pull request: just 2 lines of code. On paper, it looked insignificant. In reality, it delivered stability at scale and prevented weeks of potential downtime. This is why engineering effectiveness is so nuanced. Measuring productivity by pull requests or coding time gives you, at best, half the picture, cause it lacks context. Some fixes take hundreds of lines of code. Others require the same effort for just two. The best leaders know: one-size-fits-all engineering metrics don’t work. Impact > output. Always. I'm curious to hear how your business measures effectiveness. Do you lean on standard metrics, or build your own playbook? #EngineeringEffectiveness #CTOInsights #TechStrategy #ProductivityMetrics

𝐃𝐞𝐯𝐎𝐩𝐬 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝗣𝗹𝗮𝗻 📝 - User Story Prioritization: Align tasks with business value. - Estimation Accuracy: Gauge time and effort accurately. - Team Velocity: Measure work completed over time. - Resource Allocation: Optimize team and tools usage. 𝗖𝗼𝗱𝗲 💻 - Code Quality: Maintain high standards through reviews. - Code Effectiveness: Improve functionality and performance. - Development Speed: Accelerate delivery without sacrificing quality. - Code Reliability: Ensure robust and bug-free code. 𝗕𝘂𝗶𝗹𝗱 🛠️ - Build Frequency: Increase the number of builds for quicker feedback. - Build Success Rate: Ensure consistent, successful builds. - Build Time: Reduce the time taken to complete builds. 𝗧𝗲𝘀𝘁 🧪 - Test Coverage: Ensure comprehensive testing of code. - Defect Density: Identify and reduce the number of defects. - Test Execution Time: Speed up the testing process. - Test Success Rate: Increase the proportion of successful tests. 𝗥𝗲𝗹𝗲𝗮𝘀𝗲 & 𝗗𝗲𝗽𝗹𝗼𝘆 🚀 - Deployment Frequency: Release updates frequently. - Deployment Success Rate: Ensure successful deployments. - Change Lead Time: Reduce the time from code commit to production. - Release Cycle Time: Shorten the time between releases. 𝗢𝗽𝗲𝗿𝗮𝘁𝗲 🛠️ - Incident Response Time: Quickly resolve incidents. - Mean Time to Recovery (MTTR): Reduce the time to recover from failures. - Customer Feedback: Collect and act on user insights. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 📈 - System Uptime: Maximize the availability of services. - Performance Metrics: Track and improve system performance. - Usage Analytics: Understand and enhance user interaction. - Error Rates: Identify and lower the frequency of errors. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/ezHXZv9G https://lnkd.in/eahN4sWH 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/encdeXKB 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/eFQP-pRb #devops #engineering #softwareengineer #kubernetes #docker

A good PM knows their users. A great PM balances users, business, and technology. One framework I keep coming back to is the T-Shaped PM: The vertical bar = depth in product & customer understanding. The horizontal bar = breadth across domains like design, data, and technology. This horizontal bar is where many PMs fall short. My CEO once put it simply: a PM needs appreciation for tech. What does that mean in practice? 1. Seeing the depth of problem solving: Tech isn’t just code, it’s scalable system design. 2. Understanding prioritisation trade-offs: Why engineers pick A over B and the ripple effects. 3. Recognising risks & unknowns: Bugs and debt often reveal hidden complexity. 4. Grasping implementation logic: Not to code it, but to follow the reasoning behind it. 5. Acknowledging constraints & strengths: what your stack enables, and what it limits. How to build this horizontal bar (tech appreciation): 1. Sit in on architecture reviews and post-mortems. 2. Ask engineers to explain why a decision was made. 3. Read design docs or PRs for context. 4. Shadow a debugging session. 5. Learn core concepts like caching, retries, async flows, scalability. Why it matters: 1. Builds trust with engineering because you respect their craft. 2. Makes you a better decision-maker, seeing second-order effects of choices. 3. Keeps roadmaps credible and ambition balanced with feasibility. 4. Strengthens collaboration and you become a bridge, not a bottleneck. The biggest learning for me: building this horizontal bar is a continuous journey, not a one-time skill. Every conversation with an engineer is an opportunity to learn, and every trade-off is a chance to sharpen perspective. Would love to hear from other PMs and tech leaders: how have you grown your horizontal bar, and what practices have helped your teams build stronger appreciation for tech? Always up to swap learnings, hope these posts continue to add value ✨

6 months ago, I asked our engineering teams a simple question: "What’s slowing us down?" It wasn’t the code. It wasn’t the talent. It was small daily tasks: 🔁 Repetitive debugging 🕐 Slow pull request reviews 📝 Time-draining documentation Too much energy was lost to routine, not to real problem-solving. So we tried something new. We added Cursor AI to our daily work — not just for writing code, but also for reviews, debugging, and documentation. And we taught the team to use it as part of their normal flow. The result? We’re still building custom software, just 2.5× faster. ✔️ Fewer delays ✔️ Less overhead ✔️ Smarter code reviews ✔️ More confidence in every release Now, our engineers focus on what they do best — building real solutions. And for clients? Faster launches. Scalable products. A team that doesn’t just ship — it scales with you.

Incremental development is one of the most powerful strategies in software engineering. Instead of a “big bang” release, build the system step by step, delivering value early and often. Start with the single most important feature and ship a small, usable slice. Then increment around it (driven by real user feedback) so the product evolves smarter and closer to the market. Why it works • Lower risk and waste • Faster learning loops • Better product–market fit • Visible progress every week How to do it 1. Define the primary job-to-be-done 2. Map the smallest end-to-end path to deliver it 3. Ship with tests, monitoring, and a clear metric 4. Iterate: measure → learn → expand Rule of thumb • • Value first, plumbing later (unless compliance demands it) • • Each increment is shippable and measurable

Want to move from Software Engineer to Solutions Architect? Here’s the 3-step playbook that worked for me: 1️⃣ Master Systems Thinking: Start seeing the bigger picture. It’s not just about writing code; it’s about designing how everything talks to everything. Architecture is 80% decisions, 20% diagrams. 2️⃣ Build Leadership Skills: Learn to drive conversations, not just tickets. Speak business and tech fluently. Influence without authority. 3️⃣ Get End-to-End Project Exposure: Don’t stay siloed. Volunteer to see how features go from idea to delivery to maintenance. Own outcomes, not just outputs. This shift isn’t about years, it's about perspective. DM me if you're serious about making the move. I’ll walk you through exactly how I did it. #SolutionsArchitect #CareerSwitch #SystemThinking #TechLeadership #SoftwareEngineering

More tools were supposed to make things easier. Most teams say they didn’t. Engineering leaders keep running into the same issue: → Tools are multiplying → Context is splintered → Developers are working harder to find what they need One study found developers burn 2–3 hours weekly toggling between systems. And when they do find the dashboard? Most say they don’t trust what it shows. Meanwhile, platform teams are stuck in a loop: Building useful things, but fighting uphill battles to get teams to use them. It’s not a tooling problem anymore. It’s an experience problem. That’s where OpsLevel comes in. We've built an IDP to put everything in one clean interface: → Service ownership → One-click deploys → Live system health → Documentation and golden paths No more context-switching. No more adoption chasing. Just one place where devs can move fast and safely. If you’re scaling engineering, it’s worth asking: Is it time to unify the experience?

How technical should a Product Manager be? 👉 Short answer: Product first. Technical enough to be credible with engineering and to make high‑leverage tradeoffs. It depends on context: - Consumer/features: pattern literacy, metrics, data basics - B2B/workflows: APIs/permissions, data models, integrations - Platform/API/infra: systems, SLAs, reliability/cost/perf tradeoffs Practical baseline to aim for: 1️⃣ APIs — auth, versioning, pagination, rate limits; hands‑on with Postman/curl 2️⃣ Data — read/write simple SQL; event schemas; define success metrics and guardrails 3️⃣ Systems — latency, caching, retries, idempotency; when to go async vs sync 4️⃣ Observability — logs/metrics/traces; define and defend NFRs/SLAs with Eng 5️⃣ Delivery — environments, CI/CD, flags/rollouts/rollbacks; incident basics What you don’t need: - To ship production code. Technical credibility ≠ coding. Day‑to‑day signals you’re “technical enough”: - You anticipate edge cases, write specs with NFRs, partner in design reviews, and use telemetry to drive decisions. Level‑up path (lightweight): - 30d: Shadow on‑call/postmortems; map service boundaries; practice API calls - 60d: Spec with NFRs; instrument an event; ship a small API change end‑to‑end (with Eng) - 90d: Lead a tradeoff (cache vs recompute; sync vs async); move a reliability/latency KPI Curious to hear: In your context, where does the technical bar sit—and what skill are you building next? #ProductManagement #ProductManager #TechnicalPM #ProductLeadership #SystemDesign #APIs #Data #SQL #Observability #Reliability #Performance #SLAs #DevOps #Platform #B2B

𝗧𝗵𝗶𝗻𝗸 𝗕𝗶𝗴: 𝗔 𝗛𝗮𝗯𝗶𝘁, 𝗡𝗼𝘁 𝗮 𝗩𝗮𝗰𝗮𝘁𝗶𝗼𝗻 Are you being asked to Think Big and wondering how to do it? "𝘚𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘵𝘢𝘤𝘵𝘪𝘤𝘴 𝘪𝘴 𝘵𝘩𝘦 𝘴𝘭𝘰𝘸𝘦𝘴𝘵 𝘳𝘰𝘶𝘵𝘦 𝘵𝘰 𝘷𝘪𝘤𝘵𝘰𝘳𝘺. 𝘛𝘢𝘤𝘵𝘪𝘤𝘴 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘪𝘴 𝘵𝘩𝘦 𝘯𝘰𝘪𝘴𝘦 𝘣𝘦𝘧𝘰𝘳𝘦 𝘥𝘦𝘧𝘦𝘢𝘵." - Sun Tzu This quote perfectly captures the engineer’s dilemma. We're great at the tactical grind—writing code, fixing bugs. But how do we find time for the strategic work of "thinking big"? I've learned that you don't block out a day to think; you build it into your daily work. Here’s what one of the approaches I follow: 1. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗔𝗹𝘄𝗮𝘆𝘀. Big ideas are born from deep awareness. I spend time learning customer pain points, developer frustrations, and our long-term vision. I look at what competitors (in and out of the company) are doing to understand the strategic landscape. 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗡𝗼𝘁𝗲𝘀, 𝗡𝗼𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀. In meetings, if a short-term fix is discussed, I make a note of the long-term problem that still exists. When I fix a high severity issue, I ask if my changes could be a tool for others to solve similar bugs. These are just small observations—I collect the dots without trying to connect them yet. 3. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗗𝗼𝘁𝘀. Every week, I take a 1-hour break to review my notes. This is where I find patterns in the problems I've faced. That short-term fix I noted down? It becomes the seed for a major architectural proposal. My bug-fixing script? It’s now the start of a widely-used internal tool. Thinking big isn't a single event; it's the consistent practice of paying attention to the details and connecting them over time. It's a habit, not a vacation. #softwareengineer #thinkbig #strategicthinking #engineering

New blog post: Bridging Product and Engineering as a Staff Engineer One of the most impactful parts of the Staff+ role is navigating conversations with Product — framing trade-offs, asking the right “what if” questions, and finding alignment without slowing down delivery. In this post, I share real anecdotes on how technical leaders can create clarity, protect core systems, and still help product teams move fast. Curious to hear how you’ve approached this balance in your own roles. https://lnkd.in/gQVxuWGx