Integrating AI In Engineering Solutions

🪂 How To Make Your Design System AI-Ready (https://lnkd.in/dtnpy7CM), a practical guide on how to reduce drifts, minimize mistakes, maintain context and improve the quality of AI-generated prototypes — with structured spec files, automated auditing and token layers. Put together by Hardik Pandya from Atlassian. --- 🔹 1. Design Decisions Are Infrastructure AI-generated prototypes often don't deliver consistently decent results because of tiny inconsistencies scattered all across a design system. Often it's decisions made but not documented, hard-coded values never cleaned up, or relying too much on AI making sense of mock-ups or design flows on its own. Unsurprisingly, better AI prototypes come from better data — but also from better human guidance. We shouldn’t assume that AI knows how to choose the right component, and how to design with accessibility in mind. It needs priorities, a clear path on how we make decisions, design principles, examples, do's and don'ts. In fact, we should treat design decisions as infrastructure. That means that every time we make a decision — not just a design decision, but even decision on how actually prioritize our work and how we make decisions around here — it must find a path into the spec file that is then consumed by AI. --- 🔶 2. Three Layers: Spec Files + Token Layer + Audit To ensure quality, we establish design principles, guidelines, rules in a form of “spec files”). It's structured Markdown files that include spacing rules, color choices, component usage guidelines, priorities etc. AI is going to read and reuse that spec file every time it's going to generate a prototype. Because the spec files are text files, it's much more cost-effective, but also much more accurate just because we don't rely on AI recognizing or decoding patterns from mock-ups, but gets specific guidelines instead. In fact, extending code is often a more effective way than generating code from mock-ups. Token layer lists and keeps updated all tokens used throughout the design system. AI always chooses from a closed set of named variables instead of inventing plausible values ad-hoc. An audit script catches what AI gets wrong. It scans the prototype and flags every hard-coded value and flags it if necessary. It can be a regular software doing that, with AI waiting for its feedback to come back. Finally, when a design system ships updates, a sync routine flags which spec files need updating. The goal is to make sure that AI always reads up-to-date, current specs, not the ones written against an outdated version. --- 🔺 3. Examples of AI-Ready Design Systems ⌾ Atlassian: https://lnkd.in/dVsGc3Cp ⌾ Carbon: https://lnkd.in/d4zq4WWb ⌾ CMS Design System: https://lnkd.in/dHHzV3en ⌾ Nordhealth: https://lnkd.in/d8C4j2ZA Yet again, AI can’t magically resolve technical debt or design debt — it needs guidance, decisions, priorities and principles.

You can instantly 10x your AI-generated frontends just by learning what different UI components are called. The Component Gallery (https://component.gallery) is a comprehensive visual reference for standard UI patterns and their proper names ⚡ When delegating tasks to coding agents, the quality of your output is bottlenecked by the specificity of your input. If the only vocabulary you use in your prompts are generic words like "menu" or "button," the model will naturally return generic results. To get high-fidelity interfaces, you need the right shared language for spec-driven development. Knowing when to ask for a "combobox" instead of a "dropdown" or a "segmented control" instead of a "toggle" gives the AI the exact context it needs to execute your vision. The Component Gallery walks through this really well. UI Design Brain is also an open-source taxonomy for UI skills and component design. (https://lnkd.in/gYYbhv5q) based on the Component Gallery. Mastering this terminology is a small investment that massively upgrades your prompt engineering. #ai #programming #softwareengineering

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

McKinsey's report on 'AI Bank of the Future' - with a solid updated. What’s new? 🔹𝐌𝐨𝐫𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐨𝐧 𝐆𝐞𝐧𝐀𝐈 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 From tools ➜ to agents From analytics ➜ to orchestration From point solutions ➜ to full-stack, GenAI-native architecture At the center of it all is 𝐦𝐮𝐥𝐭𝐢𝐚𝐠𝐞𝐧𝐭 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧, where specialized AI agents are coordinated by a central AI “brain” that can plan, route tasks, and trigger the right agent at the right time. 🔹 𝐅𝐫𝐨𝐦 “𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬” 𝐭𝐨 𝐝𝐨𝐦𝐚𝐢𝐧 𝐫𝐞𝐰𝐢𝐫𝐢𝐧𝐠 Instead of building scattered pilots, the report suggests identifying a few high-impact subdomains (like credit, onboarding, or fraud) and transforming them end to end. 🔹𝐒𝐭𝐫𝐨𝐧𝐠𝐞𝐫 𝐞𝐦𝐩𝐡𝐚𝐬𝐢𝐬 𝐨𝐧 𝐫𝐞𝐮𝐬𝐞 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐞 Reusable AI components. Shared orchestration logic. AI control towers to drive governance and coordination across teams. In short: the vision is bigger, but the path is more detailed. It’s still early for most banks (and probably also most industries) to adopt a stack like this. But the ideas here: agents, orchestration, domain-level transformation are starting to feel more widely relevant. We’ve been thinking about similar challenges on our end too. 📍We recently open-sourced an internal project we’ve been building, 𝐆𝐞𝐧𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐎𝐒 - a lightweight framework for orchestrating multiagent systems. If you’re exploring in this direction, check it out: GitHub https://bit.ly/4kzE1Mt And if you're also a fan of open source, giving it a ⭐ would mean a lot to us! PS. Curious how many teams are thinking in this direction too. __________ For more on AI and open-source materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #agenticai #aiagents #artificialintelligence #technology

When L'Oréal uses AI to create new hair colors based on social media trends, they're in salons within weeks. Kraft Heinz—dead last in our study—still takes months to tweak a formula. After analyzing 26 major CPG companies at IMD's Center for Future Readiness, I discovered what separates winners from losers: The most future-ready companies treat consumer data like insider trading information. BACKGROUND: CPG in 2025 is brutal. Inflation persists. Gen-Z demands sustainability without premiums. Tariffs reshape supply chains daily. McKinsey & Company identified 150+ AI use cases for CPG transformation. Only 5 of 26 companies actually execute them. THE REVELATION: Coca-Cola didn't randomly launch Topo Chico Hard Seltzer. Their AI spotted the trend through social listening while competitors debated in boardrooms. By launch, they'd secured distribution nationwide. That's not innovation. That's prediction. What separates the top 5: L'Oréal (#1): 3.5% of sales to R&D. AI analyzes preferences real-time. Virtual try-on apps. Creates products from social trends. A 110-year company with startup velocity. The Coca-Cola Company (#2): Democratized AI internally. Every manager accesses demand forecasting. They analyze weather + social sentiment + sales simultaneously. These aren't tech companies selling beauty and beverages. They're prediction machines that happen to make products. THE WINNER'S FRAMEWORK: 1. AI at scale, not in pilots Winners integrate into workflows. Losers run demos. 2. Supply chains that anticipate Real-time visibility + AI forecasting = competitive firepower 3. D2C as intelligence goldmine 73% use multiple channels. Mine every interaction. 4. Disrupt yourself first Coca-Cola launched Costa Coffee, hard seltzers. Grew. Kraft Heinz protected legacy brands. Shrank. 5. Sustainable without premium Gen-Z spending hits $12T by 2030. They demand action at everyday prices. —— The inconvenient truth: Most CPG companies treat data like reporting instead of radar. Winners don't predict trends—they're already shipping products while competitors debate. Technological patience (knowing when to scale) + organizational agility (pivoting fast) = market domination. Three years from now, every CPG company operates like L'Oréal. Or they don't operate at all. P.S. Full Future Readiness Indicator here: https://bit.ly/3YTBzbX

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

Working with multiple LLM providers, prompt engineering, and complex data flows requires thoughtful organization. A proper structure helps teams: - Maintain clean separation between configuration and code - Implement consistent error handling and rate limiting - Enable rapid experimentation while preserving reproducibility - Facilitate collaboration across ML engineers and developers The modular approach shown here separates model clients, prompt engineering, utils, and handlers while maintaining a coherent flow. This organization has saved many people countless hours in debugging and onboarding. Key Components That Drive Success Beyond folders, the real innovation lies in how components interact: - Centralized configuration through YAML - Dedicated prompt engineering module with templating and few-shot capabilities - Properly sandboxed model clients with standardized interfaces - Comprehensive caching, logging, and rate limiting Whether you're building RAG applications, fine-tuning foundation models, or creating agent-based systems, this structure provides a solid foundation to build upon. What project structure approaches have you found effective for your generative AI projects? I'd love to hear your experiences.

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

AI holds great potential for the semiconductor industry and will kick-start the next round of innovation for faster, cheaper and more energy-efficient computation – that was my message today at SPIE Advanced Lithography + Patterning. I discussed the potential and the challenges that AI holds for our industry.   The potential is clearly huge. AI is rapidly integrated into applications, and high-performance compute is expected to underpin growth towards $1 trillion of semiconductor sales by 2030. The challenges are around the computing needs of AI models and related energy consumption. The compute workload of training a leading AI model has increased 16x every 2 years in recent years – much faster than the increase in computing power delivered by Moore’s law, which is about 2x every 2 years. The energy needed to train a leading model has not grown so steeply but still rose 10x every 2 years. This computing need has been met by building supercomputers and massive data centers. If you extrapolate these trends, training a leading AI model would need the entire world-wide electricity supply in about 10 years. That’s clearly not realistic, so the trend has to break, by training algorithms becoming more efficient and by chips becoming more efficient. In other words, the needs of AI will stimulate immense innovation in chip design and manufacturing – and the potential value of AI to our society will put urgency and funding behind that drive. As a consequence, chip makers are pulling all levers to accelerate semiconductor scaling. This includes lithographic “2D” scaling: shrinking the dimensions of transistors to pack more into a square millimeter. It will also include “3D” integration, with innovations like backside power delivery, transistor designs like gate-all-around, as well as stacking chips in the package, where holistic lithography will play a critical role to deliver performance requirements. ASML will support these trends through a comprehensive, holistic lithography portfolio. Our 0.33 NA/0.55 NA EUV lithography systems allow chip makers to shrink dimensions at the lowest possible cost on their critical layers, while tightly matched and highly productive DUV systems will continue to reduce cost. More than ever, metrology and inspections tools – whose data is fed into lithography control solutions that keep the patterning process operating within tight specs to deliver the highest possible production yields – will be essential to deliver 2D scaling and 3D integration processes. 3D integration requires wafer-to-wafer bonding, and we have demonstrated the capability to map the stresses and distortions that bonding creates and to compensate for them, reducing overlay errors for post-bonding patterning by 10x or more.   It was a pleasure catching up with the industry’s lithography and patterning experts in San Jose. I’m excited to see our collective innovation power having a go at these challenges. Together, we will push technology forward.

Uber’s internal on-call copilot, Genie, started with a RAG pipeline that sipped from 40+ security‑and‑privacy policy documents ( PDFs, Google Docs, wikis.... ) and served queries in Slack. Useful but far from reliable. SMEs flagged accuracy gaps. Answers were incomplete, context lost, tables misread. They didn’t reboot. They upgraded. ⭐ Enter Enhanced Agentic‑RAG (EAg‑RAG) 𝗘𝗻𝗿𝗶𝗰𝗵𝗲𝗱 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 They left behind flaky PDF parsers. They shifted to HTML‑formatted Google Docs, then layered in LLM‑powered enrichment: converting tables into clean markdown, injecting metadata as summaries, FAQs, keywords, identifiers. Each document chunk became searchable intelligence, not just contextless text. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 𝗔𝗻𝘀𝘄𝗲𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 They added agents before and after retrieval: • Query Optimizer breaks down or clarifies vague questions • Source Identifier narrows which docs matter • BM25 + vector search surfaces the best chunks • Post‑Processor de‑dupes and reorders context logically This combo meant go‑to‑answer accuracy shot up +27%, wrong advice dropped 60% all within critical security & privacy channels. 𝗟𝗟𝗠‑𝗮𝘀‑𝗝𝘂𝗱𝗴𝗲 They broke the SME bottleneck. By feeding queries, responses, and benchmarks to an LLM judge, they achieved automated batch evaluation which aligned with SME standards in minutes, not weeks. Why this matters: Operational AI is about tooling so reliable that SMEs use it without second‑guessing. Uber didn’t train a model but they layered structure, signals, and evaluation to treat knowledge as an operating system, not just data. They made the documentation better. They made the retrieval smarter. They made evaluation instantaneous. That’s systems thinking. That’s what separates an AI pilot from a one‑and‑done prototype. Check this gem in the Uber Engineering Blog post: https://lnkd.in/gUFDt9ug The image below shows end-to-end workflow of EAg-RAG architecture of this implementation