Feedback Mechanisms in Engineering

In just 30 days, defects dropped, morale increased... And our roadmap conversations shifted from “𝘐 𝘵𝘩𝘪𝘯𝘬 𝘸𝘦 𝘴𝘩𝘰𝘶𝘭𝘥 ” to “𝘏𝘦𝘳𝘦’𝘴 𝘸𝘩𝘢𝘵 𝘸𝘦 𝘬𝘯𝘰𝘸.” Every engineering leader wants to get the most out of their team, but it’s easy to lose sight of what really drives them: feedback. I learned this the hard way. I launched a product that was all hype, but there was nothing from the users. I quickly realized: engineers need to see the impact of their work. Without feedback, it’s all guesswork and that leads to frustration. Here’s how I turned things around: 𝐒𝐡𝐚𝐫𝐞 𝐭𝐡𝐞 𝐑𝐞𝐚𝐥 𝐓𝐚𝐥𝐤: I started recording customer calls and sharing the raw moments, the “wow!” reactions and frustrations. Engineers connect with that energy way more than bullet points. 𝐒𝐮𝐩𝐩𝐨𝐫𝐭’𝐬 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Instead of just letting support tickets pile up, we held quick 5-minute debriefs each sprint to highlight recurring issues that specs missed. 𝐎𝐧-𝐂𝐚𝐥𝐥 𝐄𝐦𝐩𝐚𝐭𝐡𝐲: Every quarter, we had an engineer join the on-call rotation. Waking up at 3 AM to fix a bug you wrote? That’s a whole new level of ownership. 𝐈𝐧𝐯𝐨𝐥𝐯𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐄𝐚𝐫𝐥𝐲: Before features hit Jira, we brought engineers into discovery calls. Hearing the “why” from customers helped them think critically before the code was even written. The results? 30 days later, defects dropped, morale improved, and our roadmap shifted from gut feeling guesses to data driven decisions. Feedback loops are the key to growth. Start today.

The semiconductor manufacturing flow includes testing at critical points to weed out defective dies and package assemblies. After fabrication, wafers are tested (also called wafer probe or wafer sort). The good dies are assembled into packages at the assembly site and then final-tested. For cost or capability reasons, each of these facilities is often physically separate, operated by different entities and even located in different countries altogether. The manufacturing flow for a device might look like the following: 1️⃣ Wafer Fabrication at an IDM fab in the USA 2️⃣ Wafer Sort at a probe house in Taiwan 3️⃣ Package Assembly at an OSAT in the Philippines 4️⃣ Final Test at an IDM test site in Malaysia Despite several intermediate inspection steps, defective package assemblies can still reach the final test site. For example, a wirebond package with a wire defect introduced during the mold process might not be detected until final testing, which could be days or even weeks later. One effective screen to prevent such escapes is 100% open-short testing at the assembly site. This approach helps to: 1) Stop defective parts from leaving the assembly site 2) Immediately identify and sequester any maverick lots 3) Provide fast feedback to the errant assembly process (die attach, wirebonding, molding, etc.) for improvement While screening is no substitute for process and quality improvement—as my friends in quality engineering often remind me—it helps catch obvious issues early. A short feedback loop drives corrective action, improves yields, and avoids the cost and delay of final-testing parts with known open/short issues.

The Most Powerful Concept in Building Automation Isn’t “Control” It’s FEEDBACK—guided by INTENT In a BAS fundamentals class, we were asked: “What’s the most powerful word in building automation?” A classmate answered CONTROL. That’s a great answer—it’s the C in DDC. But here’s the evolution that happens when we move from learning systems to mastering them 👇 🧭 INTENT – The “Why” Defined in the Sequence of Operations (SOO) This answers: • What should the system do? • Under what conditions? • In what order? • With what priorities? Without intent, control has no direction. 🕹️ CONTROL – The “Action” Implemented through: • Boolean / Numeric Writable points • Priority Arrays • Schedules, enables, overrides Control is the command we issue to the system. 👀 FEEDBACK – The “Truth” Provided by: • Sensors (Temp, Flow, Pressure, CO₂) • Equipment feedback (status, speed, position) • Niagara point status: {ok}, {fault}, {down}, {stale} Feedback is how we know the system responded. Why this matters: • Control without feedback = guessing • Feedback without intent = noise • Intent without control = documentation only When all three align, BAS becomes intelligent, not just automated. Control is the steering wheel. Feedback is your vision. Intent is the destination. If you want to master BAS—not just operate it—start thinking in Intent, Control, and Feedback.

🚀 ML Systems Don’t Improve Automatically. Feedback Loops Drive Progress As I continue exploring production ML systems, one important realization has become clear: Deploying and monitoring a model is not enough. For a system to remain effective, it must continuously learn and adapt. 🧠 The Missing Component In many ML workflows, we focus on: - training models - deploying them - monitoring performance But a critical question often gets overlooked: How does the system improve over time? ⚙️ The Role of Feedback Loops Feedback loops enable ML systems to evolve by: - collecting real-world data from user interactions - capturing outcomes and ground truth signals - identifying errors and mispredictions - retraining models with updated data They transform a static model into a continuously learning system. ⚠️ The Risk Without Feedback Without well-designed feedback mechanisms: - models become outdated as data distributions shift - performance gradually degrades - Systems fail to adapt to new patterns - Re-training becomes reactive and inefficient The system loses its ability to stay relevant. 🧠 Key Insight A high-performing ML system is not just accurate, it is adaptive and self-improving. Because in dynamic environments, maintaining performance requires continuous learning. ⚙️ What I’m Focusing On I’m now prioritizing: - designing robust feedback pipelines - capturing reliable real-world signals - automating retraining and updates - Closing the loop between predictions and outcomes 🚀 Final Thought In production ML: 👉 Models remain static 👉 Systems evolve through feedback and iteration If you’re building ML systems: 👉 How do you incorporate feedback into your pipeline? #MachineLearning #MLOps #AIInfrastructure #MLSystems #SystemDesign #DataEngineering #LearningInPublic