Engineering Product Development Stages

📚 Key Test Documentation Types 1. Test Plan Purpose: Outlines the overall strategy and scope of testing. Includes: Objectives Scope (in-scope and out-of-scope) Resources (testers, tools) Test environment Deliverables Risk and mitigation plan Example: "Regression testing will be performed on modules A and B by using manual TC" 2. Test Strategy Purpose: High-level document describing the overall test approach. Includes: Testing types (manual, automation, performance) Tools and technologies Entry/Exit criteria Defect management process 3. Test Scenario Purpose: Describes a high-level idea of what to test. Example: "Verify that a registered user can log in successfully." 4. Test Case Purpose: Detailed instructions for executing a test. Includes: Test Case ID Description Preconditions Test Steps Expected Results Actual Results Status (Pass/Fail) 5. Traceability Matrix (RTM) Purpose: Ensures every requirement is covered by test cases. Format: Requirement ID Requirement Description Test Case IDs REQ_001 Login functionality TC_001, TC_002 6. Test Data Purpose: Input data used for executing test cases. Example: Username: testuser, Password: Password123 7. Test Summary Report Purpose: Summary of all testing activities and outcomes. Includes: Total test cases executed Passed/Failed count Defects raised/resolved Testing coverage Final recommendation (Go/No-Go) 8. Defect/Bug Report Purpose: Details of defects found during testing. Includes: Bug ID Summary Severity / Priority Steps to Reproduce Status (Open, In Progress, Closed) Screenshots (optional) Here's a set of downloadable, editable templates for essential software testing documentation. These are useful for manual QA, automation testers, or even team leads preparing structured reports. 📄 1. Test Plan Template File Type: Excel / Word Key Sections: Project Overview Test Objectives Scope (In/Out) Resources & Roles Test Environment Schedule & Milestones Risks & Mitigation Entry/Exit Criteria 🔗 Download Test Plan Template (Google Docs) 📄 2. Test Case Template File Type: Excel Columns Included: Test Case ID Module Name Description Preconditions Test Steps Expected Result Actual Result Status (Pass/Fail) Comments 🔗 Download Test Case Template (Google Sheets) 📄 3. Requirement Traceability Matrix (RTM) File Type: Excel Key Fields: Requirement ID Requirement Description Test Case ID Status (Covered/Not Covered) 🔗 Download RTM Template (Google Sheets) 📄 4. Bug Report Template File Type: Excel Columns: Bug ID Summary Severity Priority Steps to Reproduce Actual vs. Expected Result Status Reported By 🔗 Download Bug Report Template (Google Sheets) 📄 5. Test Summary Report File Type: Word or Excel Includes: Project Name Total Test Cases Execution Status (Pass/Fail) Bug Summary Test Coverage Final Remarks / Sign-off 🔗 Download Test Summary Template (Google Docs) #QA

The Medical Device Iceberg: What’s hidden beneath your product is what matters most. Your technical documentation isn’t "surface work". It’s the foundation that the Notified Body look at first. Let’s break it down ⬇ 1/ What is TD really about? Your Technical Documentation is your device’s identity card. It proves conformity with MDR 2017/745. It’s not a binder of loose files. It’s a structured, coherent, evolving system. Annexes II & III of the MDR guide your structure. Use them. But make it your own. 2/ The 7 essential pillars of TD: → Device description & specification → Information to be supplied by the manufacturer → Design & manufacturing information → GSPR (General Safety & Performance Requirements) → Benefit-risk analysis & risk management → Product verification & validation (including clinical evaluation) → Post-market surveillance Each one matters. Each one connects to the rest. Your TD is not linear. It’s a living ecosystem. Change one thing → It impacts everything. That’s why consistency and traceability are key. 3/ Tips for compiling TD: → Use one “intended purpose” across all documents → Apply the 3Cs: ↳ Clarity (write for reviewers) ↳ Consistency (same terms, same logic) ↳ Connectivity (cross-reference clearly) → Manage it like a project: ↳ Involve all teams ↳ Follow MDR structure ↳ Trace everything → Use “one-sheet conclusions” ↳ Especially in risk, clinical, V&V docs ↳ Simple, precise summaries → Avoid infinite feedback loops: ↳ One doc, one checklist, one deadline ↳ Define “final” clearly 4/ Best practices to apply: → Add a summary doc for reviewers → Update documentation regularly → Create a V&V matrix → Maintain URS → FRS traceability → Hyperlink related docs → Provide objective evidence → Use searchable digital formats → Map design & mfg with flowcharts Clear TD = faster reviews = safer time to market. Save this for your next compilation session. You don't want to start from scratch? Use our templates to get started: → GSPR, which gives you a predefined list of standards, documents and methods. ( https://lnkd.in/eE2i43v7 ) → Technical Documentation, which gives you a solid structure and concrete examples for your writing. ( https://lnkd.in/eNcS4aMG )

AI Prototyping Tools Masterclass: If you've been bouncing between v0, Bolt, Replit, and Lovable wondering, "Which one should I actually be using?" You're not alone. They all look impressive. But if you don’t understand what each one actually does best, you're just spinning your wheels. So, let’s break it all down: — ONE - The 4 Major Players (and What They’re Built For) Let me remind you, these aren’t just "tools" anymore. They’re fast-evolving cloud development environments And each one has a clear edge. 1. v0 by Vercel This one’s all about beautiful front-end design - out of the box. Clean UIs, polished interactions, and a $3.25B valuation behind it. Perfect if you’re spinning up a demo for stakeholders... And want something that looks amazing fast. Just don’t expect deep backend stuff without plugging in extras like Supabase. 2. Bolt Built for speed. The CEO told us the whole thing runs in the browser, no VMs & no lag. That's the reason it went from $0 to $40M ARR in just 6 months. If you’re testing ideas fast (think 10-minute prototypes), this is your tool. It’s flexible, but you'll need to connect things like a database yourself. 3. Replit This one goes deep. Founded by Amjad Masad and now valued at $1.16B, Replit gives you full-stack power. Built-in auth, built-in database, built-in deployment. If your prototype needs to function like a real product, this is the play. It’s not as slick as v0 or as lightning-fast as Bolt... But when it comes to handling real logic, Replit is in a league of its own. 4. Lovable Lovable is becoming the most loved "vibe coding" tool. Founded by Antonin Osika, and it hit $17M ARR in just 3 months. Honestly? It’s the easiest tool in the game, especially if you don’t code. Drag, drop, sync with Supabase. That’s it. No setup headaches. No complex environment. Perfect for non-technical PMs or anyone who wants to go... From idea to live prototype without touching a line of code. — TWO - ADJACENT TOOLS But wait, there’s a twist. These tools aren’t where AI prototyping stops. There are adjacent tools you’ll want to layer in depending on your skill level: If you’re just looking to generate quick code or play around with ideas: → ChatGPT and Claude work great. But if you want to build something real (and you can code): → Tools like Cursor, Windsurf, Zed, and GitHub Copilot are insanely powerful. A great flow in my experience so far? Start in Bolt or Lovable → Sync to GitHub → Then build deeper in Cursor. — I broke all this down in my latest newsletter drop: "Ultimate Guide to AI Prototyping Tools (Lovable, Bolt, Replit, v0)" If you want to understand how to actually use these tools and which one fits your workflow best, go here: https://lnkd.in/eRypMZQ8 It’ll save you weeks of trial and error.

My 10 mistakes introducing PLM. 🚩  1. Lack of clear objectives PLM initiatives start without a precise definition of: - What exactly should be improved (e.g., change processes, data quality, time-to-market, …)? - How success will be measured? - How do I balance diverging targets: function, integration, technology? - ALM, PLM and ERP are the most important IT-Systems along the PLC. How are functions and processes distributed and integrated? ➡️ Consequence: The project loses focus, becomes bloated, or fails due to unrealistic expectations. 🚩  2. Treating PLM as an IT project PLM is fundamentally a process and organizational transformation, not just a software. ➡️ Consequence: Poor involvement of departments leads to low adoption and inefficient workflows. 🚩  3. Unclear or conflicting processes Companies often attempt to implement PLM while their underlying processes: - do not exist, - are poorly documented, - differ across organizational units. ➡️ Consequence: The tool ends up digitizing chaos instead of improving it. 🚩  4. Scope too large / Big-Bang implementation Trying to deploy a comprehensive PLM system all at once is one of the most common pitfalls. ➡️ Consequence: Delays, budget overruns, and user frustration. 🚩  5. Insufficient Change Management PLM affects roles, responsibilities, and daily work habits. Common oversights: - weak communication, - missing training, - lack of key-user involvement, - lack of C-level involvement. ➡️ Consequence: Resistance, workarounds, and low acceptance. 🚩  6. Poor master data and document quality - inconsistent or duplicated data, - no data cleanup before migration, - missing standards (naming, numbering, classification, ...). ➡️ Consequence: Bad data stays bad—only now inside an expensive system. 🚩  7. Over-customization Companies frequently try to model every exception and satisfy every request. ➡️ Consequence: Complex, costly, hard-to-maintain systems that hinder upgrades. 🚩  8. Underestimating integration PLM relies on clean interfaces to systems like: CRM, CAD, ALM, ERP, MES, SCM. ➡️ Consequence: Media breaks, duplicate data, and process gaps. 🚩  9. Insufficient resources or the wrong project team PLM is often done “on the side": - no dedicated project manager, - limited internal PLM expertise, - weak executive sponsorship. ➡️ Consequence: Delays and unsatisfied never ending stories 🚩  10. Focusing only on basic design features Many PLM deployments center solely on CAD and E-BOM. But PLM should cover: requirements management, variant management, change management, service, ... ➡️ Consequence: PLM becomes an expensive CAD data vault rather than an enterprise-wide product backbone or PLM functions are taken over by CAD (Onshape) or ERP ✅ Summary Most pitfalls arise not from technology or functional coverage, but from strategy, processes, and change management. Organizations often underestimate the cultural and organizational change—and overestimate what the software alone can fix.

Types of Documents: A clear explanation of the documents mostly used in construction, engineering, or technical project environments. These documents are essential for ensuring quality, progress, coordination, and compliance: 1. IR – Inspection Request A formal request submitted by the contractor to notify the consultant or client that a certain portion of work is complete and ready for inspection. Purpose: To get approval before proceeding to the next stage. 2. MIR – Material Inspection Request Submitted to inspect materials delivered to the site to ensure they comply with specifications and standards. Purpose: To approve the use of materials before installation. 3. Submittals Documents submitted for review and approval before execution. Includes: ·      Material Submittals ·      Shop Drawings ·      Method Statements Purpose: To ensure all work aligns with project requirements and specifications. 4. FIR – Field Inspection Request Similar to IR, but specifically for on-site inspections, often used for civil and structural works. Purpose: Confirm proper execution of work at the field level. 5. RFI – Request for Information Sent by the contractor to the consultant/client to clarify ambiguous drawings, specifications, or instructions. Purpose: To resolve design or scope conflicts during construction. 6. NCR – Non-Conformance Report Issued when work or materials do not comply with approved specifications or standards. Purpose: To identify, document, and correct mistakes or quality issues. 7. FCR – Field Change Request A document used to propose a change in the original design or scope due to site conditions or client needs. Purpose: To seek approval for modifications in fieldwork. 8. Pre-Qualification Documents Submitted by contractors or suppliers before bidding or project participation. Includes: Company profile Past project experience Financial strength Certifications Purpose: To prove eligibility and capability to handle the project. 9. Work Release Form Official authorization given to proceed with specific tasks or activities. Purpose: Ensures that no work begins without formal approval. 10. SQN – Site Query Note (also called Site Instruction or Site Query Notice) Raised when site conditions differ from drawings or specifications. Purpose: To clarify site-related technical or design issues. 11. Daily Reports Logs the day-to-day activities on-site, including: Weather conditions Number of workers Work completed Equipment used Purpose: Progress monitoring and recordkeeping. 12. Weekly Reports Summarizes daily progress, issues, manpower, equipment, and pending items. Often includes photos and planned vs actual comparison. Purpose: Weekly project tracking and reporting to management or clients. 13. Monthly Reports Comprehensive report including: Progress summary Project timeline updates Financial status Risk assessments Purpose: High-level review for stakeholders and decision-makers. #HME #QAQC #MEP

Wow. I just built 3 mini-apps for PMs in under 10 minutes: an empathy mapper, a journey analyzer, and a competitive analysis tool with Opal (Google Labs). No PRD. No Figma. No tickets. Just an idea → an experience. Instead of debating documents, I’m now sharing working mini-apps with my team ask them "react to this, let’s refine it” I used Opal to prototype the vibe with an: -Empathy Mapper -User Journey Analyzer -Competitive Landscape Tool Each one took minutes. Each one was immediately shareable. Each one changed the conversation. Use Opal when: -You want to validate an idea before writing a PRD -You need a quick tool for a workshop or meeting -You want to make research or concepts visible -You want to better empathize about your user Think of Opal as your 10-minute lab. If it takes longer than that, move it to a full prototype — that’s where other AI prototyping tools come in. Tips for PMs adopting this workflow -Start tiny. Your first Opal app should take under ten minutes. That constraint keeps you focused on intent, not polish. -Think in verbs, not nouns. Prompts like “summarize feedback” or “visualize trends” produce far better prototypes than static descriptions. -Collaborate live. Invite designers, engineers, and stakeholders into the session. Watching the prototype evolve creates alignment faster than any meeting. -Reflect. After every prototype, note what worked. Each build sharpens your prompting instincts and your product intuition. 🔗 Guides + masterclass in the comments 👇

10Rs in the Product Life Cycle 🌎 The transition to a circular economy requires a structured approach to rethinking how products are designed, used, and managed at end-of-life. The 10R framework offers a comprehensive set of strategies to guide this transformation across all phases of the product life cycle. Each of the 10Rs represents a specific action aimed at reducing resource use, extending product longevity, or recovering value. When applied systematically, these strategies support both environmental goals and operational efficiency. In the design and production phase, the focus is on preventing unnecessary resource consumption. This includes refusing materials or products that are not essential, redesigning systems to minimize waste, and reducing inputs through improved efficiency. The use phase is centered on maximizing the lifespan and performance of products and components. This involves strategies such as reusing existing products, repurposing them for different functions, repairing damage, refurbishing outdated models, and remanufacturing to restore functionality. In the after-use phase, the goal shifts toward recovering value from materials that can no longer be used as-is. Recycling enables the reprocessing of materials into new inputs, while regeneration supports the renewal of natural systems and resources. By aligning the 10Rs with the stages of the product life cycle, organizations can identify targeted opportunities to reduce environmental impact and strengthen supply chain resilience. This approach also enables more informed decisions at every stage—from product development to disposal—helping businesses align sustainability with performance and long-term value creation. Source: Ellen MacArthur Foundation #sustainability #sustainable #business #esg #climatechange #circulareconomy #circular

Product development entails inherent risks where hasty decisions can lead to losses, while overly cautious changes may result in missed opportunities. To manage these risks, proposed changes undergo randomized experiments, guiding informed product decisions. This article, written by Data Scientists from Spotify, outlines the team’s decision-making process and discusses how results from multiple metrics in A/B tests can inform cohesive product decisions. A few key insights include:  - Defining key metrics: It is crucial to establish success, guardrail, deterioration, and quality metrics tailored to the product. Each type serves a distinct purpose—whether to enhance, ensure non-deterioration, or validate experiment quality—playing a pivotal role in decision-making.  - Setting explicit rules: Clear guidelines mapping test outcomes to product decisions are essential to mitigate metric conflicts. Given metrics may show desired movements in different directions, establishing rules beforehand prevents subjective interpretations during scientific hypothesis testing.  - Handling technical considerations: Experiments involving multiple metrics raise concerns about false positive corrections. The team advises applying multiple testing corrections for success metrics but emphasizes that this isn't necessary for guardrail metrics. This approach ensures the treatment remains significantly non-inferior to the control across all guardrail metrics. Additionally, the team proposes comprehensive guidelines for decision-making, incorporating advanced statistical concepts. This resource is invaluable for anyone conducting experiments, particularly those dealing with multiple metrics. #datascience #experimentation #analytics #decisionmaking #metrics – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gewaB9qC

One of the hottest topics in AI is evals (evaluations). Effective Humans + AI assessment of outputs is essential for building scalable self-improving products. Here is the case being laid out for evals in product development. 🔥 Evals are the hidden lever of AI product success. Evaluations—not prompts, not model choice—are what separate mediocre AI products from exceptional ones. Industry leaders like Kevin Weil (OpenAI), Mike Krieger (Anthropic), and Garry Tan (YC) all call evals the defining skill for product managers. 🧭 Evals define what “good” means in AI. Unlike traditional software tests with binary pass/fail outcomes, AI evals must measure subjective qualities like accuracy, tone, coherence, and usefulness. Good evals act like a “driving test,” setting criteria across awareness, decision-making, and safety. ⚙️ Three core approaches dominate evals. PMs rely on three methods: human evals (direct but costly), code-based evals (fast but limited to deterministic checks), and LLM-as-judge evals (scalable but probabilistic). The strongest systems blend them—human judgments set the gold standard, while LLM judges extend coverage and scalability. 📐 Every strong eval has four parts. Effective evals set the role, provide the context, define the goal, and standardize labels/scoring. Without this structure, evals drift into vague “vibe checks.” 🔄 The eval flywheel drives iteration speed. The intention should be to drive a positive feedback loop where evals enable debugging, fine-tuning, and synthetic data generation. This cycle compounds over time, becoming a moat for successful AI startups. 📊 Bottom-up metrics reveal real failure modes. While common criteria include hallucination, safety, tone, and relevance, the most effective teams identify metrics directly from data. Human audits paired with automated checks help surface the real-world patterns generic metrics often miss. 👥 Human oversight keeps AI honest. LLM-as-judge systems make evals scalable, but without periodic human calibration, they drift. The most reliable products maintain a human-in-the-loop review loop—auditing eval results, correcting blind spots, and ensuring that automated judgments remain aligned with real user expectations. 📈 PMs must treat evals like product metrics. Just as PMs track funnels, churn, and retention, AI PMs must monitor eval dashboards for accuracy, safety, trust, contextual awareness, and helpfulness. Declining repeat usage, rising hallucination rates, or style mismatches should be treated as product health warnings. Some say this case is overstated, and point to the lack of reliability of evals or the relatively low current in use in AI dev pipelines. However this is largely a question of working out how to do them well, especially effectively integrating human judgment into the process.

Most AI ideas die before they even get off the ground. Why? Because teams get stuck in endless debates instead of building something tangible. The best way to get leadership buy-in, align teams, and validate your AI concept? Prototyping. But here’s the secret—you don’t need to code to prototype AI effectively. Instead of diving into AI coding tools like Cursor or Replit, you can use no-code AI prototyping tools like Notion AI, UX Pilot, CustomGPTs, and Voiceflow to move even faster. In our latest AI Community Learning Series, Polly M Allen (Ex-Principal PM, Alexa AI) and Rupa Chaturvedi (AI UX Leader, ex-Amazon, Google, Uber) shared how to: ✅ Align teams faster with interactive AI prototypes (instead of lengthy PRDs) ✅ Use no-code tools to build AI-powered experiences—without writing a single line of code ✅ Pick the right AI use cases and avoid overcomplicating solutions Plus, they demoed how to build a Shopping AI Assistant live—showing exactly how to structure, test, and refine AI interactions in minutes. Curious how they did it? Full recap + session replay 👇 Have you built an AI prototype before? What worked (or didn’t)? Share your thoughts below! #ProductManagement #AI #Design #Prototyping