Engineering Online Learning Resources

Building useful Knowledge Graphs will long be a Humans + AI endeavor. A recent paper lays out how best to implement automation, the specific human roles, and how these are combined. The paper, "From human experts to machines: An LLM supported approach to ontology and knowledge graph construction", provides clear lessons. These include: 🔍 Automate KG construction with targeted human oversight: Use LLMs to automate repetitive tasks like entity extraction and relationship mapping. Human experts should step in at two key points: early, to define scope and competency questions (CQs), and later, to review and fine-tune LLM outputs, focusing on complex areas where LLMs may misinterpret data. Combining automation with human-in-the-loop ensures accuracy while saving time. ❓ Guide ontology development with well-crafted Competency Questions (CQs): CQs define what the Knowledge Graph (KG) must answer, like "What preprocessing techniques were used?" Experts should create CQs to ensure domain relevance, and review LLM-generated CQs for completeness. Once validated, these CQs guide the ontology’s structure, reducing errors in later stages. 🧑⚖️ Use LLMs to evaluate outputs, with humans as quality gatekeepers: LLMs can assess KG accuracy by comparing answers to ground truth data, with humans reviewing outputs that score below a set threshold (e.g., 6/10). This setup allows LLMs to handle initial quality control while humans focus only on edge cases, improving efficiency and ensuring quality. 🌱 Leverage reusable ontologies and refine with human expertise: Start by using pre-built ontologies like PROV-O to structure the KG, then refine it with domain-specific details. Humans should guide this refinement process, ensuring that the KG remains accurate and relevant to the domain’s nuances, particularly in specialized terms and relationships. ⚙️ Optimize prompt engineering with iterative feedback: Prompts for LLMs should be carefully structured, starting simple and iterating based on feedback. Use in-context examples to reduce variability and improve consistency. Human experts should refine these prompts to ensure they lead to accurate entity and relationship extraction, combining automation with expert oversight for best results. These provide solid foundations to optimally applying human and machine capabilities to the very-important task of building robust and useful ontologies.

Every time you ask your AI a question, it forgets everything it learned the last time you asked. That's how most AI knowledge systems work today. They're called RAG systems — Retrieval Augmented Generation. You upload documents. The AI searches fragments on every query. Builds an answer from scratch. Closes the session. Next question? Same process. Zero accumulation. Andrej Karpathy — the person who built Tesla's AI vision system and co-founded OpenAI — published something quietly last week that might change this fundamentally (https://lnkd.in/dNB5swS8). He calls it "LLM Wiki." The concept: Instead of searching your documents every time, the LLM builds and maintains a persistent knowledge base. A structured wiki of markdown files with cross-references, concept pages, and contradiction flags. New document arrives? The LLM doesn't just index it. It reads it, extracts the key information, updates existing pages, notes where new data contradicts old claims, and strengthens the evolving synthesis. His key insight: "The tedious part of maintaining a knowledge base is not the reading or the thinking — it's the bookkeeping." Cross-references. Keeping summaries current. Noting when new data overrides old claims. That's exactly what nobody in your team has time for. And exactly what LLMs don't get bored doing. What this looks like in practice: Step 1 — Pick one knowledge domain. Not everything. One area where your team wastes time re-finding information. Customer onboarding. Product specs. Compliance requirements. Step 2 — Set up the structure. Claude Projects or Obsidian + Claude Code as the wiki layer. Raw sources go in one folder. The LLM-maintained wiki lives in another. Step 3 — Feed sources one at a time. Let the LLM summarize, cross-reference, and file. You review. Redirect. Ask follow-up questions. The wiki grows with every session. Step 4 — Query against the wiki, not the raw documents. Answers are faster, more contextual, and cite specific pages. The open-source project Graphify already implements this pattern — claiming 70x fewer tokens needed to answer questions compared to raw-folder RAG. For now, this is still a developer concept. No plug-and-play enterprise solution exists yet. The benchmarks are anecdotal, not peer-reviewed. The wiki could drift or hallucinate if not curated. But the direction is clear: AI that builds knowledge, not AI that searches it. Save this for when your team's knowledge base fails you again. — 📌 Save this post for later ♻️ Share it to inspire your network Follow Hartmut Hübner, PhD for AI insights that work.

Engineering transformation is not optional anymore, it’s a race against irrelevance! For years, we’ve all seen the same patterns in product development. - Mechanical, E/E and software teams working in isolation. - Complexity growing faster than our ability to manage it. - Errors discovered too late. - Interfaces that don’t fit. Integration often feels like assembling a puzzle… only to realize that half the pieces were built from entirely different pictures. Weeks, sometimes months, lost not because of bad engineering, but because of fragmented engineering. And yet, despite knowing these problems for years, many organizations are still waiting. Waiting for the “right moment.” Waiting for clearer standards. Waiting for others to move first. That moment is gone. Global competitors have already picked up speed and are exerting pressure. With Model-Based Systems Engineering (MBSE) and AI reaching real maturity, we finally have the tools to fix what we’ve been complaining about for a decade. The question is no longer if transformation will happen. The question is: how fast can you move? Here’s how Vlad and I currently think about it in 9 concrete steps: 1. Adopt and mature MBSE - Build system models that truly reflect your product, not just documentation. 2. Derive domain-specific models from system models - Create consistent, hierarchical product structures across all domains and disciplines. 3. Capture all engineering artifacts From requirements (RFLP) over testing to homologation, make everything explicit and create development templates. 4. Link all artifacts via a knowledge graph - Enable impact chain analysis based on a solid engineering ontology. 5. Standardize and accelerate component development - Align tools, data and processes for each discipline and component 6. Build cross-domain CI/CD pipelines - Enable fast, automated iteration across requirements, architecture, design, simulation and testing. 7. Rationalize the toolchain (APIs over UIs) - Tools must be controllable from the outside enabling agent-based workflows. 8. Make engineering knowledge machine-readable - Document not just the what, but the how and why. Only then can agents effectively navigate engineering-specific challenges. 9. Define the future work split - Clarify what engineers do and what AI agents should handle. Establish strong human-in-the-loop validation. The core message is simple: Engineering excellence in the future will not come from better tools alone. It will come from how well we connect systems, data, people and agents. Companies that start building this foundation now will gain speed. Those who wait will struggle to catch up. What’s missing from your perspective? Which steps would you add to make this transformation truly work? Timmo Sturm | Daniel Spiess | Sebastian Linzmair | Sascha Bach | Rick Bouter

Is your AI current enough? Most teams treat freshness like an infrastructure setting. A refresh cadence. An indexing schedule. A pipeline job. That sounds operational. It is actually strategic. The real question comes earlier: how fast does the knowledge behind this system change, and what happens when the answer comes from yesterday’s truth instead of today’s? If you skip that decision, the architecture will look fine right up to the moment it fails. A policy changes. A product update goes live. An operational signal shifts. The system still answers confidently from an old state of knowledge. Freshness Decision Framework: Start with knowledge velocity, not tooling. If the domain is stable, static stores can work well. If the domain changes often, the system needs update mechanisms that match that rate of change, whether that is scheduled refresh, continuous ingestion, or event-triggered re-indexing. Then go one level deeper. Who owns the knowledge? Who flags changes? Can teams trace an answer back to the exact knowledge state the system used at that moment? That is not a nice-to-have. It is the basis of governance when outputs influence audited decisions. Key Takeaways: • Map knowledge velocity - Classify domains by how quickly truth changes before selecting retrieval or reasoning architecture. • Match update mechanisms - Use refresh patterns that fit real-world change rates, not engineering convenience. • Assign knowledge ownership - Make specific teams responsible for signaling updates and validating critical content. • Build traceable answers - Ensure the system can show what it knew, when it knew it, and why it answered that way. Knowledge freshness is not just a pipeline problem. It is a governance choice that shapes the whole system. #KnowledgeManagement, #AIArchitecture, #Governance, #EnterpriseAI, #ContentStrategy

𝐀𝐫𝐞 𝐘𝐨𝐮 𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐒𝐚𝐦𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐓𝐰𝐢𝐜𝐞? You read the docs, debug the issue, learn the pattern. Six months later you're googling the exact thing you already solved. The problem isn't taking notes. It's building a searchable engineering memory system. Why do engineers love Obsidian? • Local-first Markdown files your notes live on your disk, not someone's cloud. • Bi-directional linking with [[Notes]] builds connections automatically. • Graph-based knowledge organization turns isolated notes into a brain. • Git-friendly and version controllable. • Works offline, no vendor lock-in, future-proof. Your notes are plain text. Your knowledge is permanent. What does the engineering knowledge workflow look like? • Capture: Collect ideas, notes, and learnings every day. • Connect: Link related concepts and build context. • Retrieve: Find insights instantly when you need them. • Reuse: Apply knowledge to build, write, and share. Link [[Kafka]] to [[Event-Driven Architecture]] to [[System Design]] to [[Distributed Systems]], and your notes stop being a pile and start being an interconnected knowledge graph. What can you build with it? • Personal engineering wiki. • System design repository. • DSA and interview prep vault. • AI research notebook. • Debugging knowledge base. • DevOps runbooks and API reference system. Which plugins level you up? • Dataview: Query notes like a database. • Excalidraw: Draw architecture diagrams inline. • Kanban: Visual task management. • Tasks: Track tasks across notes. • Git: Version control built in. • Calendar: Daily notes and planning. Each pass through the loop learn, capture, connect, retrieve, build makes the next one faster. That's the compounding effect of a connected knowledge system. The best engineers aren't the ones who know everything. They're the ones who can retrieve everything they've already learned. Your memory fades. A searchable knowledge graph doesn't. Stop solving the same problem twice. Build the second brain. What system do you use to organize your engineering knowledge? ♻️ Repost this to help your network get started ➕ Follow Sivasankar Natarajan for more #Obsidian #SecondBrain #Productivity

𝗜𝗳 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘄𝗮𝗹𝗸𝘀 𝗼𝘂𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗽𝗹𝗮𝗻𝘁, 𝘀𝗼 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗲𝗱𝗴𝗲. For years, the most valuable lessons in manufacturing lived in people’s heads, sat in spreadsheets, or got buried in reports no one read. KM systems existed — but insights stayed siloed. Maintenance logs didn’t inform production. Quality data never reached design. And when experts left, their know-how left too. KBE helped. Rules were codified, designs automated, tasks accelerated. But it stayed narrow. Useful for engineering but useless for the wider enterprise. Now Smart Factories are rewriting the rules. The “smart” in Smart Manufacturing is no longer just IoT, AI, or digital twins — it’s Knowledge Management evolving into the core ingredient that makes factories adaptive, resilient, and truly smart. This transformation is a true 𝗛𝘂𝗺𝗮𝗻–𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻–𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 (𝗛𝗢𝗧)  shift: • Organizations evolve into networks that learn. • Employees grow into knowledge partners. • Technology connects, structures, and scales those learnings. 𝗔𝘁 𝘁𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗹𝗲𝘃𝗲𝗹: From hierarchies to networks. Lessons flow horizontally across production, logistics, and supply chains — and vertically into ERP and PLM, the true nerve centers. PLM connects design and engineering with shop-floor feedback, ensuring learnings inform product evolution, not just production routines. The enterprise itself becomes a learning system as every captured lesson strengthens resilience, speed, and customer value. 𝗔𝘁 𝘁𝗵𝗲 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝗹𝗲𝘃𝗲𝗹: From operators to knowledge workers. Every workaround, every fix, every idea feeds the system. From machine cooperation to human–machine collaboration. Cobots and AI extend capability, people bring judgment. From one-time training to continuous learning. KM guides workers in real time, embedding best practices into daily decisions. 𝗔𝘁 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗹𝗲𝘃𝗲𝗹: 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 – Every event, fix, and insight is logged with context. 𝗘𝗻𝗿𝗶𝗰𝗵 – AI structures it, links it, and connects it with past cases. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 – Learnings move across functions and levels. 𝗔𝗽𝗽𝗹𝘆 – Best practices feed back into workflows, tools, and training. 𝗘𝘃𝗼𝗹𝘃𝗲 – Each cycle makes the system smarter. Documentation, once a burden, is now the nervous system. It captures memory, creates best practices, and feeds them forward — so tomorrow’s decisions are always better than yesterday’s. In Smart Manufacturing, knowledge isn’t just an asset but the very definition of smart. Winners will be those who capture lessons, turn them into best practices, and scale them across the ecosystem. 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝗻 𝗼𝗻𝗲 𝗹𝗶𝗻𝗲: Tribal Know-How → Scattered Learnings → Codified Rules → Knowledge-Enhanced Practices → Ecosystem-Wide Best Practices Ref:https://lnkd.in/dTiqaCQu

Much of the conversation about enterprise AI focuses on model quality, prompt engineering, and optimization. These are real problems. The engineering challenge I spend most of my time on is less discussed and more consequential. Entity resolution across heterogeneous enterprise data sources. The same person appears in at least six different forms across a typical executive's work stack: a name in email, an email address in calendar, a nickname in Slack, a full name and title in CRM, a display name in Zoom, and some variation of all of the above in shared documents. The same project might be "Q3 initiative" in one meeting, "the Atlas project" in another, and "that thing we decided in August" in a third. If you can't resolve these into a unified, consistent representation, you are not building a knowledge system. What you are building is a fast way to retrieve fragments that may or may not refer to the same things. Fun facts to know and tell. A knowledge-centric architecture depends on getting these things straight. A model of an executive's work is only useful if the entities that model references are correctly identified and consistently maintained. A commitment attributed to the wrong person, or a project split across three different representations, means the system is reasoning over a flawed picture. The LLM will produce confidently incorrect output based on a flawed picture. The executive will quickly lose trust. Twenty-five years of building enterprise systems taught me this: the unglamorous work of data modeling, entity resolution, and pipeline reliability are where trust is won or lost. The demo shines with its pristine data. Production data is a different planet. Entity resolution is the prerequisite for everything else in a knowledge-centric architecture. It's where the deepest defensibility comes from. When a system is able to correctly resolve your professional world into a structured graph, that system won't easily be replicated by competitors starting from scratch.

One of the evergreen promises—and persistent challenges—of knowledge management in AEC is scaling expert knowledge. In a business built on expertise and results, firms need to help emerging professionals become more effective healthcare architects, bridge engineers, project managers, or sustainability experts. Historically, that kind of learning has happened slowly: one project at a time, one mentor at a time. But today, the urgency is rising. Many firms are facing a real-time talent crunch: senior experts are retiring, hiring is competitive, and there’s pressure to ramp up new staff faster than ever. That’s where KM teams are stepping in—not just to collect or store knowledge, but to accelerate the transfer of critical insight from experts to emerging professionals. One increasingly popular method is structured expert interviews. Here's an example: https://lnkd.in/guFC-9CR Instead of asking a senior architect to write down everything they know (which rarely works), KM teams are capturing informal conversations on video, then using AI tools to transcribe, summarize, and transform them into searchable, reusable knowledge. In some cases, those interviews also form the basis for courses or training programs. These interviews take different forms. Sometimes they’re one-on-one conversations recorded in a conference room or over Zoom. Other times, they’re held as live learning events—inviting staff to listen in, ask questions, and absorb the exchange in real time. In some firms, experts are even interviewing one another, creating space for reflection and storytelling while modeling a culture of shared learning. Regardless of format, the resulting videos become assets that can be reused across onboarding, training, and AI-powered search. KM teams act as knowledge brokers, working across departments and generations to extract tacit expertise and make it available in the flow of work. That includes surfacing bite-sized insights through search, packaging repeatable methods into standards, or embedding lessons learned into onboarding programs. AI is playing a major role throughout this process—making transcription and summarization faster, enabling retrieval through tools like AI search, and helping turn raw insights into new knowledge assets for experts to review. But the shift is as much cultural as it is technical. The best KM teams are creating lightweight, repeatable ways to scale expert knowledge without putting all the burden on the experts themselves. 💡 This is Trend 8 of 12 from the Issue 6 of Smarter by Design: “How Leading AEC Knowledge Management Teams Are Evolving to Thrive in the AI Era.” 📖 👉 Read the full issue: https://lnkd.in/gYgrFzVN #AEC #KnowledgeManagement #SmarterByDesign

Outdated knowledge assets aren't just an administrative issue—it's a safety and quality issue that affects everyone. Growing concerns and the loss of experienced personnel are creating more and more pain oints across manufacturing. Many recognize that knowledge management must be a strategic focus for accelerating workforce competency, protecting operational performance, and driving innovation. But moving from a strategic focus to embedding it within the cultute of an organization is where it gets tricky. In my conversations with manufacturing leaders, organizations that are most successful at maintaining current documentation have gone deeper than just building systems—they've created cultures that genuinely value knowledge sharing and accuracy. One way to support this cultural shift is by creating a "knowledge health index"—a dashboard showing the status of knowledge assets across operations. Some of The most effective systems include: 🔁 Collecting feedback from users on documentation quality and accuracy, in a non-intrusice way as soon as the job is done. 📈 Monitoring usage patterns to identify which procedures are most frequently accessed 🫶Mature capabilities that can automatically flag documentation that may need updates based on system changes 🤝Create a way to gauge how often knowledge assets are being reviewed and updated - and recognizing those who lead by example. One manufacturer who implementing this approach has maintained over 95% accuracy in their critical knowledge assets, compared to less than 60% before implementation. The difference between organizations that struggle with knowledge management and those that leverage knowledge to create value isn't just adopting technology—it's creating an environment where everyone understands that knowledge is a shared asset that requires collective stewardship. The LNS Research Industrial Knowledge Management framework provides a scalable approach for manufacturers, regardless of where you are in your journey. What approaches have you seen work for maintaining critical operational knowledge in your organization? 📣 I'd love to hear your experiences in the comments. ⬇️ #KnowledgeManagement #IndustrialTransformation #OperationalExcellence #ConnectedWorkforce #DigitalTransformation

The Hidden Cost of Engineering Knowledge Fragmentation Engineering teams are drowning in data. PLM systems hold millions of documents—CAD files, technical specs, test reports—but finding the right information at the right time remains a huge challenge. 🚧 The Problem? Engineers waste countless hours searching across siloed systems. Critical IP gets lost in legacy documents. Teams reinvent the wheel, unknowingly redoing work that already exists. Institutional knowledge disappears as employees leave. These inefficiencies slow down innovation, increase costs, and delay product launches. ⚙️ How can we fix this? AI-driven search and knowledge transformation can extract, organize, and connect engineering knowledge—making it discoverable within seconds. At BrahmaSumm, we’re tackling this challenge by: ✅ Using semantic search and clustering to surface relevant knowledge. ✅ Automating tribal knowledge capture to preserve expertise. ✅ Integrating with PLM systems for seamless access to engineering data. ✅ Running on efficient infrastructure—designed for scalability and security. The goal? Turn engineering documentation into an asset, not a bottleneck. Would love to hear how your teams are handling knowledge discovery today. What strategies have worked (or not worked) for you?