Impact of LLMs on Engineering Processes

We know LLMs can substantially improve developer productivity. But the outcomes are not consistent. An extensive research review uncovers specific lessons on how best to use LLMs to amplify developer outcomes. 💡 Leverage LLMs for Improved Productivity. LLMs enable programmers to accomplish tasks faster, with studies reporting up to a 30% reduction in task completion times for routine coding activities. In one study, users completed 20% more tasks using LLM assistance compared to manual coding alone. However, these gains vary based on task complexity and user expertise; for complex tasks, time spent understanding LLM responses can offset productivity improvements. Tailored training can help users maximize these advantages. 🧠 Encourage Prompt Experimentation for Better Outputs. LLMs respond variably to phrasing and context, with studies showing that elaborated prompts led to 50% higher response accuracy compared to single-shot queries. For instance, users who refined prompts by breaking tasks into subtasks achieved superior outputs in 68% of cases. Organizations can build libraries of optimized prompts to standardize and enhance LLM usage across teams. 🔍 Balance LLM Use with Manual Effort. A hybrid approach—blending LLM responses with manual coding—was shown to improve solution quality in 75% of observed cases. For example, users often relied on LLMs to handle repetitive debugging tasks while manually reviewing complex algorithmic code. This strategy not only reduces cognitive load but also helps maintain the accuracy and reliability of final outputs. 📊 Tailor Metrics to Evaluate Human-AI Synergy. Metrics such as task completion rates, error counts, and code review times reveal the tangible impacts of LLMs. Studies found that LLM-assisted teams completed 25% more projects with 40% fewer errors compared to traditional methods. Pre- and post-test evaluations of users' learning showed a 30% improvement in conceptual understanding when LLMs were used effectively, highlighting the need for consistent performance benchmarking. 🚧 Mitigate Risks in LLM Use for Security. LLMs can inadvertently generate insecure code, with 20% of outputs in one study containing vulnerabilities like unchecked user inputs. However, when paired with automated code review tools, error rates dropped by 35%. To reduce risks, developers should combine LLMs with rigorous testing protocols and ensure their prompts explicitly address security considerations. 💡 Rethink Learning with LLMs. While LLMs improved learning outcomes in tasks requiring code comprehension by 32%, they sometimes hindered manual coding skill development, as seen in studies where post-LLM groups performed worse in syntax-based assessments. Educators can mitigate this by integrating LLMs into assignments that focus on problem-solving while requiring manual coding for foundational skills, ensuring balanced learning trajectories. Link to paper in comments.

Until recently, I would’ve bet that PLC and DCS logic would be the last frontier untouched by LLMs. Now, I’m not so sure. A new research paper from ABB, “Spec2Control: Automating PLC/DCS Control-Logic Engineering from Natural Language Requirements with LLMs – A Multi-Plant Evaluation”, takes a major step forward. The authors demonstrate how Large Language Models can generate IEC 61131-3 compliant control code directly from natural-language specifications. Things like: “Open valve V-203 if tank level > 80% and pump P-401 is off.” Across four industrial plants, the system achieved: → 86–91% first-pass functional accuracy → 55% reduction in engineering hours for repetitive logic → 40% faster acceptance testing with human validation in the loop The models didn’t just translate text. They reasoned about control logic, detected missing conditions, and flagged unsafe interlocks. Spec2Control hints at a future where engineers design through intent, not syntax. Where control narratives, standards, and logic are part of a single intelligent workflow. And where “AI-assisted control engineering” becomes a practical reality, not a conference buzzword. The question isn’t if this will reshape control engineering, but how soon it will become standard practice. What do you think? Will AI-generated control logic become trusted across regulated industries like chemicals and energy, or will safety and accountability concerns keep it on the sidelines? #industry40 #ai #manufacturing #automation #plc

From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future ... The landscape of software engineering is rapidly evolving, driven by advancements in artificial intelligence. A recent research paper titled "From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges, and Future" sheds light on the transformative role of Large Language Models (LLMs) and their evolution into LLM-based agents. ✅ This paper is a must-read for anyone interested in the intersection of AI and software development.  👉 Key Insights from the Research  1. Understanding LLMs vs. LLM-based Agents The paper clarifies the distinction between LLMs and LLM-based agents: - LLMs are powerful tools for text generation and understanding, but they lack autonomy. - LLM-based agents integrate LLMs with external tools, enabling them to perform complex tasks autonomously, such as debugging and code refactoring.  2. Enhanced Capabilities in Software Engineering LLM-based agents demonstrate significant advantages: - They can autonomously debug, refactor, and generate tests, leading to increased efficiency and reduced human error. - These agents adapt to changing requirements, making them invaluable in dynamic software environments.  3. Key Areas of Application The research highlights several critical applications: - Requirement Engineering: Automating the capture and analysis of software requirements. - Code Generation: Streamlining the development process by generating code snippets from natural language descriptions. - Software Security: Enhancing security protocols and detecting vulnerabilities through proactive measures.  4. Challenges and Limitations Despite their promise, LLMs and LLM-based agents face challenges: - Context Length: Limited context can hinder their performance on extensive codebases. - Hallucinations: The risk of generating plausible but incorrect outputs necessitates human oversight to ensure accuracy.  5. Future Directions and Research Opportunities The paper discusses the need for: - Standardization and Benchmarking: Establishing unified standards to evaluate LLM-based agents effectively. - Exploration of AGI: Investigating the potential of LLM-based agents to approach Artificial General Intelligence, which could revolutionize software engineering practices.  👉 Conclusion This research paper provides a comprehensive overview of how LLMs and LLM-based agents are reshaping software engineering. By understanding their capabilities, applications, and limitations, we can better harness their potential to enhance productivity and innovation in the field. I encourage industry professionals to read this paper and engage in discussions about the future of AI in software engineering. What are your thoughts on the role of LLMs and LLM-based agents in our industry? Let's connect and explore this fascinating topic together.

I’ve been building and managing data systems at Amazon for the last 8 years. Now that AI is everywhere, the way we work as data engineers is changing fast. Here are 5 real ways I (and many in the industry) use LLMs to work smarter every day as a Senior Data Engineer: 1. Code Review and Refactoring LLMs help break down complex pull requests into simple summaries, making it easier to review changes across big codebases. They can also identify anti-patterns in PySpark, SQL, and Airflow code, helping you catch bugs or risky logic before it lands in prod. If you’re refactoring old code, LLMs can point out where your abstractions are weak or naming is inconsistent, so your codebase stays cleaner as it grows. 2. Debugging Data Pipelines When Spark jobs fail or SQL breaks in production, LLMs help translate ugly error logs into plain English. They can suggest troubleshooting steps or highlight what part of the pipeline to inspect next, helping you zero in on root causes faster. If you’re stuck on a recurring error, LLMs can propose code-level changes or optimizations you might have missed. 3. Documentation and Knowledge Sharing Turning notebooks, scripts, or undocumented DAGs into clear internal docs is much easier with LLMs. They can help structure your explanations, highlight the “why” behind key design choices, and make onboarding or handover notes quick to produce. Keeping platform wikis and technical documentation up to date becomes much less of a chore. 4. Data Modeling and Architecture Decisions When you’re designing schemas, deciding on partitioning, or picking between technologies (like Delta, Iceberg, or Hudi), LLMs can offer quick pros/cons, highlight trade-offs, and provide code samples. If you need to visualize a pipeline or architecture, LLMs can help you draft Mermaid or PlantUML diagrams for clearer communication with stakeholders. 5. Cross-Team Communication When collaborating with PMs, analytics, or infra teams, LLMs help you draft clear, focused updates, whether it’s a Slack message, an email, or a JIRA comment. They’re useful for summarizing complex issues, outlining next steps, or translating technical decisions into language that business partners understand. LLMs won’t replace data engineers, but they’re rapidly raising the bar for what you can deliver each week. Start by picking one recurring pain point in your workflow, then see how an LLM can speed it up. This is the new table stakes for staying sharp as a data engineer.

I work at Airbnb where I write 99% of my production code using LLMs. Spotify's CEO recently announced something similar. I mention my employer not because my workflow is sponsored by them, but to establish a baseline for the massive scale, reliability constraints, and code quality standards this approach has to survive. Many engineers abandon LLMs because they run into problems instantly, but these problems have solutions. If you're a skeptic, please read and let me know what you think. 𝗧𝗵𝗲 𝘁𝗼𝗽 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗮𝗿𝗲:  • 𝗖𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗿𝗲𝗳𝗮𝗰𝘁𝗼𝗿𝘀 (generated code is really bad or broken)  • 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 (the model doesn’t know your codebase, libraries, apis..etc)  • 𝗣𝗼𝗼𝗿 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 (the model doesn’t implement what you asked for)  • 𝗗𝗼𝗼𝗺 𝗹𝗼𝗼𝗽𝘀 (the model can’t fix a bug and tries random things over and over again)  • 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗹𝗶𝗺𝗶𝘁𝘀 (inability to modify large codebases or create complex logic) In this article, I show how to solve each of these problems by using the LLM as a a force multiplier for your own engineering decisions instead of a random number generator for syntax.

🔍 Another massive analysis of 457 LLMOps case studies - and wow, this is the real-world implementation data we've been missing. After sifting through 600,000+ words of technical documentation, we've distilled the actual engineering patterns that work in production. Not theoretical architectures or proof-of-concepts, but battle-tested implementations across enterprises, startups, and everything in between. Key insights that jumped out: - RAG isn't just about throwing vectors in a database - companies like Doordash achieved 90% hallucination reduction through careful quality control - Fine-tuning smaller models often outperforms larger ones in production (with receipts from multiple companies showing 5-10x cost reductions) - The shift from basic prompting to sophisticated orchestration isn't just hype - it's driving real metrics What makes this particularly valuable: Each case study breaks down the nitty-gritty technical decisions teams made, from model selection to infrastructure choices. It's essentially a massive knowledge transfer from teams who've already solved these problems. Deep dive here: https://lnkd.in/dRv-cs5J Seriously worth a read if you're implementing LLMs in production or planning to. The summaries alone are worth their weight in GPU hours 🚀 #LLMOps #MLEngineering #ProductionAI #GenerativeAI #TechArchitecture P.S. Would love to hear from others who've tackled similar challenges - what patterns have you found most effective in production?

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale. Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments. Here are the insights that stood out👇 1.🔸Enterprise LLM integration is a data architecture problem The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads. 2.🔸Scaling LLMs requires pattern-level thinking They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook. 3.🔸Performance is not just about bigger models The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable. 4.🔸Real enterprise value comes from operational rigor Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data. 5.🔸AI teams win by mastering both concepts and impact The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact A clear, structured way to think about turning LLM capabilities into business outcomes. If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book. Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications. #LLM

Many current LLMs are multimodal, meaning they can process multiple media simultaneously, such as text+image or text+video. But what about 3D and CAD? I experimented with image generation and attempted to have GPT+Dall-E capture the design intent of an engineering drawing, but the results were underwhelming. So, what's the real state of play? While the applications are obvious and some initial models are emerging, let's dive deeper. I did some research and these are my findings: 𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: • Text-to-3D and Image-to-3D generation for quick design creation • AI-assisted ideation and creativity support • Shape optimization for specific criteria • Rapid design exploration and variation generation • Automated support for assisted CAD modeling and engineering drawings 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀: • Precision challenges: Most models work with meshes or voxels, not boundary representation (B-rep) needed for high-precision engineering • Design intent: State-of-the-art language models struggle to fully capture complex engineering design intent. They're great for applications where creativity is important, but not so good for high-precision mechanics • Early stage development: Even promising projects like Autodesk's Project Bernini are in their infancy. The startup World labs is about to accelerate.   𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: There are interesting and promising approaches on the horizon, but they're far from being democratized. CAD engineers currently cannot expect the same broad range of support as is currently the case with LLMs in other fields. And with the performance like in the video, genAI will not take the jobs of CAD engineers.

Most teams building with LLMs hit the same wall: You add more context → accuracy improves → so you add even more context. Then latency spikes. Outputs get inconsistent. Debugging becomes guesswork. That’s not a model problem. It’s a context problem. I wrote a post introducing a framework for thinking about context as a constrained design surface, not an infinite buffer. The core idea: ▶️ Context has diminishing returns ▶️ Different kinds of context fail in different ways ▶️ Past a certain point, more context actively makes models worse The post breaks context into: ▶️ Cold (policies, schemas, invariants) ▶️ Warm (memory, preferences, summaries) ▶️ Hot (user input, tool output, scratchpads) …and shows why treating them all the same is one of the biggest causes of hallucinations, latency, and inconsistency in production AI systems. If you've faced similar issues and solved the problem in different ways, I'd love to hear from you! A link to the full blog post is in the first comment. #genai #contextengineering #llm #ai

𝐂𝐭𝐫𝐥+𝐀𝐥𝐭+𝐃𝐞𝐥𝐞𝐭𝐞 𝐃𝐞𝐯 𝐂𝐚𝐫𝐞𝐞𝐫?⁣ LLMs Are Changing Software Engineering. ⁣ In recent months, I’ve been experimenting with LLMs across nearly every part of software engineering — prompting API scaffolds, debugging code, writing tests, generating infra files, even watching autonomous agents build full features end-to-end.⁣ The results are quite Impressive… and honestly, a little unsettling.⁣ ⁣ After 20+ years in this field, I’ve seen shifts — OOAD, mobile, cloud, Agile, DevOps, SaaS. Microservices...⁣ But this rise of LLMs and autonomous agents feels different.⁣ Not just faster — it’s fundamentally reshaping what it means to build.⁣ ⁣ I’m still figuring out what it means to be a developer in this new era — and how to guide others without pretending to have all the answers.⁣ We used to say software design is an art — a mix of intuition, structure, and elegance. But now AI generates not just code, but visual art, music, and prose. So maybe the art isn’t gone. It’s just changed. Maybe the real artistry now lies in how we prompt, guide, and critique the systems we build with... 🍺 Also on Constellar blog here: https://lnkd.in/gbv6YhWT #AI #LLMs #softwareengineering #developers #career #reflection