Generative AI Use Cases

Developing a 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 can quickly become overwhelming without a solid foundation. A messy structure leads to inefficiency, making scaling and collaboration difficult.  𝗪𝗵𝗲𝗿𝗲 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗕𝗲𝗴𝗶𝗻?   To streamline development, I’ve designed a 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 that prioritizes 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻.  𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲  ✅ 𝗰𝗼𝗻𝗳𝗶𝗴/ – YAML-based configurations to separate settings from code.   ✅ 𝘀𝗿𝗰/ – Modularized core logic, including 𝗹𝗹𝗺/ and 𝗽𝗿𝗼𝗺𝗽𝘁_𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴/ components.   ✅ 𝗱𝗮𝘁𝗮/ – Organized storage for embeddings, prompts, and datasets.   ✅ 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀/ – Ready-to-use scripts for real-world use cases (e.g., chat sessions, prompt chaining).   ✅ 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀/ – Jupyter notebooks for rapid experimentation and analysis.  𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁  🔹 Use 𝗬𝗔𝗠𝗟 for clean, readable configurations.   🔹 Implement 𝗲𝗿𝗿𝗼𝗿 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 & 𝗹𝗼𝗴𝗴𝗶𝗻𝗴 for efficient debugging.   🔹 Apply 𝗿𝗮𝘁𝗲 𝗹𝗶𝗺𝗶𝘁𝗶𝗻𝗴 to manage API consumption effectively.   🔹 Maintain a 𝗰𝗹𝗲𝗮𝗿 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 for flexibility.   🔹 Optimize performance through 𝘀𝗺𝗮𝗿𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗰𝗮𝗰𝗵𝗶𝗻𝗴.   🔹 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 to ensure seamless team collaboration.   🔹 Leverage 𝗝𝘂𝗽𝘆𝘁𝗲𝗿 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀 for quick experimentation before production deployment.  𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗦𝘁𝗮𝗿𝘁𝗲𝗱  • Clone the repository & install dependencies.   • Configure your model using the provided YAML files (**config/**).   • Explore 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀/ for real-world implementations.   • Utilize 𝗝𝘂𝗽𝘆𝘁𝗲𝗿 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀 for fine-tuning and testing.      𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗧𝗶𝗽𝘀   ✔ Follow 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 to keep your codebase clean.   ✔ Write 𝘂𝗻𝗶𝘁 𝘁𝗲𝘀𝘁𝘀 for new components to ensure reliability.   ✔ Monitor 𝘁𝗼𝗸𝗲𝗻 𝘂𝘀𝗮𝗴𝗲 & 𝗔𝗣𝗜 𝗹𝗶𝗺𝗶𝘁𝘀 to optimize costs.   ✔ Keep 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 for easy scalability.  By adopting this structured approach, you can 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝘄𝗿𝗲𝘀𝘁𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻.  How do you structure your 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 projects? Share your thoughts in the comments!  

Most people want to use Generative AI. Fewer know how to build it. Even fewer know how to build it right. That’s where a roadmap like this becomes essential. I just went through this detailed Generative AI Roadmap, and it lays out a learning path from fundamentals all the way to deploying AI agents and real-world apps. If you're serious about building GenAI skills, here’s what’s included: - Start with core concepts: supervised vs. unsupervised learning, overfitting, basic Python, matrix ops, probability - Move into generative modeling: RNNs, autoencoders, latent space, backprop, VAEs - Deep dive into GANs & diffusion models: StyleGAN, CycleGAN, Stable Diffusion, U-Nets - Explore LLMs for text generation: transformers, attention, prompt engineering, few-shot learning - Go beyond text: music, audio, synthetic data, 3D generation - Learn fine-tuning techniques: LoRA, PEFT, instruction tuning - Then get hands-on with deployment: containerization, quantization, APIs, scaling - And finally, build AI agents with LangChain, CrewAI, and n8n—tying perception, reasoning, and action into workflows This roadmap is perfect for developers, ML engineers, and even product teams looking to understand what it really takes to go from an idea to a working GenAI app. -- Join our Newsletter with 137K Subscribers — www.theravitshow.com

Generative AI has taken “AI” out of the hands of specialists and placed it into every industry that has a problem to solve. When teams can build with natural language, integrate with existing systems, and map human intent instead of rigid filters, AI stops being a lab project and becomes a business capability. I was going through some recent case studies from Publicis Sapient and one of them really stood out to me. It captures something important about where we are in this GenAI wave. We finally have AI that is not limited to technical teams. It is being used directly to reshape customer experience in ways that people can actually feel. The Homes and Villas by Marriott Bonvoy project is a great example of this shift. Publicis Sapient and Marriott built a generative search experience using Azure OpenAI that turns natural language intent into real, bookable vacation homes. Not filters, not rigid queries. Actual human intent. A few technical details from the case study that I loved: ✦ Intent parsing over keyword search Travelers can describe feelings or preferences. The system uses LLMs to infer constraints, property attributes, and destination suggestions across 150K listings. ✦ GPT based retrieval pipeline LLMs enrich the query, expand candidates, and rerank results based on nuanced signals which reduces dead ends and increases high confidence matches. ✦ Real time context generation Weather, activities, and travel ideas are synthesized for each result which turns simple search into discovery. ✦ Enterprise scale rollout acceleration Once the pattern was built, Marriott cut expansion time from a year to three months which shows how GenAI lowers the cost of experimentation inside large organizations. If you want to dive deeper into the Marriott project and the system behind it, the full Publicis Sapient customer story is a great read: https://lnkd.in/evGBTTTN

Generative AI is a complete set of technologies that work together to provide intelligence at scale. This stack includes the foundation models that create text, images, audio, or code. It also features production monitoring and observability tools that ensure systems are reliable in real-world applications. Here’s how the stack comes together: 1. 🔹Foundation Models At the base, we have models trained on large datasets, covering text (GPT, Mistral, Anthropic), audio (ElevenLabs, Speechify, Resemble AI), 3D (NVIDIA, Luma AI, Open Source), image (Stability AI, Midjourney, Runway, ClipDrop), and code (Codium, Warp, Sourcegraph). These are the core engines of generation. 2. 🔹Compute Interface To power these models, organizations rely on GPU supply chains (NVIDIA, CoreWeave, Lambda) and PaaS providers (Replicate, Modal, Baseten) that provide scalable infrastructure. Without this computing support, modern GenAI wouldn’t be possible. 3. 🔹Data Layer Models are only as good as their data. This layer includes synthetic data platforms (Synthesia, Bifrost, Datagen) and data pipelines for collection, preprocessing, and enrichment. 4. 🔹Search & Retrieval A key component is vector databases (Pinecone, Weaviate, Milvus, Chroma) that allow for efficient context retrieval. They power RAG (Retrieval-Augmented Generation) systems and keep AI responses grounded. 5. 🔹ML Platforms & Model Tuning Here we find training and fine-tuning platforms (Weights & Biases, Hugging Face, SageMaker) alongside data labeling solutions (Scale AI, Surge AI, Snorkel). This layer helps models adjust to specific domains, industries, or company knowledge. 6. 🔹Developer Tools & Infrastructure Developers use application frameworks (LangChain, LlamaIndex, MindOS) and orchestration tools that make it easier to build AI-driven apps. These tools connect raw models and usable solutions. 7. 🔹Production Monitoring & Observability Once deployed, AI systems need supervision. Tools like Arize, Fiddler, Datadog and user analytics platforms (Aquarium, Arthur) track performance, identify drift, enforce firewalls, and ensure compliance. This is where LLMOps comes in, making large-scale deployments reliable, safe, and clear. The Generative AI Stack turns raw model power into practical AI applications. It combines compute, data, tools, monitoring, and governance into one seamless ecosystem. #GenAI

I stopped using Claude as a chatbot. I started using it as my Product Design team. 🚀 Most designers use AI for generating copy, rewriting text, or creating random UI ideas. That's only scratching the surface. While working on an AI Interview Engine project, I realized Claude can contribute to almost every stage of product design from problem discovery to developer handoff. Today, my workflow looks very different. How I use Claude to ship AI-powered products 🌱1. Discover & Research - User pain points - Competitor analysis - Market research - Interview questions - Research synthesis Instead of spending hours organizing notes, Claude helps me identify patterns and opportunities faster. 💡 2. Product Thinking & Strategy - PRDs - Feature prioritization - User journeys - Edge cases - Success metrics This is where Claude becomes powerful. Not because it gives answers. Because it helps me ask better questions. 🎯 3. UX Flows & Information Architecture - User flows - Task flows - Journey maps - States and scenarios - Error handling Many UX problems appear before a single screen is designed. 🎨 4. Claude Design + UI Creation - Screen concepts - UX critiques - Design system recommendations - Interaction ideas - Accessibility checks Claude helps me explore more possibilities before committing to a direction. ⚡ 5. Claude Code: This changed my workflow completely. I use it for: - Frontend prototypes - Design system implementation - UX validation - Product simulations - Documentation generation Seeing ideas come alive in code helps uncover issues much earlier. 🌻6. Validation & Iteration - Heuristic reviews - UX audits - Edge case testing - Scenario generation - Accessibility review The goal is not to validate designs. The goal is to validate decisions. 🚀 7. Handoff & Delivery - Functional requirements - Developer documentation - Acceptance criteria - Component behavior - Interaction specifications Developers get more clarity and fewer assumptions. The biggest lesson? AI didn't replace my design process. It amplified it. The more product thinking, judgment, and decision-making I bring, the better the output becomes. That's why I believe the future belongs to designers who can combine: 🧠 Product Thinking 🤖 AI Leverage 🎨 Design Craft 📈 Business Understanding Not just screen design. What part of your design process are you currently using Claude for? 👇 I'd love to learn from your workflow too. #uxdesign #productdesign #uidesign #claudeai #claudecode #artificialintelligence #designsystems #uxresearch #figma #designleadership #aidesign #productdesigner #userexperience #designthinking #aitools #aiindesign #ai #aidesigntools #designercommunity # #juniordesigners #learning #linkedin #creator #uiux

ChatGPT Created a Fake Passport That Passed a Real Identity Check A recent experiment by a tech entrepreneur revealed something that should concern every security leader. ChatGPT-4o was used to create a fake passport that successfully bypassed an online identity verification process. No advanced design software. No black-market tools. Just a prompt and a few minutes with an AI model. And it worked. This wasn't a lab demonstration. It was a real test against the same kind of ID verification platforms used by fintech companies and digital service providers across industries. The fake passport looked legitimate enough to fool systems that are currently trusted to validate customer identity. That should make anyone managing digital risk sit up and pay attention. The reality is that many identity verification processes are built on the assumption that making a convincing fake ID is difficult. It used to require graphic design skills, access to templates, and time. That assumption no longer holds. Generative AI has lowered the barrier to entry and changed the rules. Creating convincing fake documents has become fast, easy, and accessible to anyone with an internet connection. This shift has huge implications for fraud prevention and regulatory compliance. Know Your Customer processes that depend on photo ID uploads and selfies are no longer enough on their own. AI-generated forgeries can now bypass them with alarming ease. That means organizations must look closely at their current controls and ask if they are still fit for purpose. To keep pace with this new reality, identity verification must evolve. This means adopting more advanced and resilient methods like NFC-enabled document authentication, liveness detection to counter deepfakes, and identity solutions anchored to hardware or device-level integrity. It also requires a proactive mindset—pressing vendors and partners to demonstrate that their systems can withstand the growing sophistication of AI-driven threats. Passive trust in outdated processes is no longer an option. Generative AI is not just a tool for innovation. It is also becoming a tool for attackers. If security teams are not accounting for this, they are already behind. The landscape is shifting fast. The tools we trusted even a year ago may not be enough for what is already here. #Cybersecurity #CISO #AI #IdentityVerification #KYC #FraudPrevention #GenerativeAI #InfoSec https://lnkd.in/gkv56DbH

I recently spent some time traveling across Switzerland, and like many others, I chose to explore the country primarily by train. What struck me wasn’t just the breathtaking landscapes or the precision of the rail system, it was how seamless the entire experience felt. From planning routes to hopping between cities, everything worked like a well-orchestrated product. And it got me thinking: this is what the future of travel could look like: supercharged by AI. Here are a few reflections through a product lens: 🚆 1. Frictionless Planning → Autonomous Itineraries Today, we still toggle between apps, reviews, and maps to plan trips. Tomorrow, AI could generate dynamic, end-to-end itineraries based on your preferences, budget, weather, and even mood continuously optimizing in real time. 🧭 2. Context-Aware Travel Companions Imagine an AI co-pilot that knows you're tired after a long journey and suggests a quieter route, pre-orders your meal, or nudges you toward a hidden gem nearby, without you asking. ⏱️ 3. Real-Time Adaptability Swiss trains are famously punctual, but when things don’t go as planned, AI can instantly re-route you, rebook connections, and notify stakeholders. No stress, no scrambling. 🌍 4. Hyper-Personalized Exploration Every traveler is different. AI can move us away from “top 10 things to do” toward deeply personalized experiences, whether you're a history buff, foodie, or someone chasing solitude. 🔄 5. Travel as a Continuous Experience, Not a Series of Transactions What Switzerland does beautifully is integrate multiple legs of a journey into one cohesive flow. AI can take this further, connecting discovery, booking, transit, and experience into a single, evolving journey. As a product manager, this trip reminded me that the best experiences are the ones that feel invisible, where the system just works. Switzerland showed what great infrastructure can do. AI will define what intelligent infrastructure looks like. Curious to hear, what’s one travel pain point you’d love AI to solve?

Creating a Proof of Concept Generative AI app is deceptively easy. In a few hours, you can hack together a prototype that meets 60% of your requirements using a framework like LangChain. However, managing expectations around the finished product's timelines is crucial. The complexity increases exponentially beyond the initial phase. Achieving 90% of your product requirements demands rigorous test-driven development to expose and protect against risks. Sourcing for a wide variety of real user questions (both in-domain and out-of-domain), setting the temperature to 0, and using the seed parameter in the OpenAI API (https://lnkd.in/gahRjUKr) can significantly enhance the predictability and testability of your code changes. Addressing the final 10% is the most challenging. It rarely involves direct solutions. Instead, it's about identifying and blocking undesirable interactions. Each Large Language Model (LLM) has its peculiarities, presenting unpredictable and non-repeatable edge cases. These issues range from protection against prompt injections to difficulties maintaining domain restrictions. I've previously outlined some strategies to tackle these challenges in a detailed post: https://lnkd.in/gjFTMpbR A strong collaboration between technology and business teams is essential for developing these systems successfully. We engineers must adopt a product-centric mindset, communicate openly, and stay agile to adopt best practices as they continuously evolve. Everyone in this field is learning as we go, and I'll continue to share my insights on building production and customer-facing applications as I discover them. Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

From Meh to Mind-Blowing: A Kano Model Hack for Generative AI Let's talk about something I've been thinking about lately: how generative AI (Gen AI) can transform businesses and why the Kano Model is the perfect lens to prioritize its adoption. Gen AI isn't new, but its explosion into the mainstream (think ChatGPT, Gemini) has turned it into a game-changer. The real question isn't if to use it, but how to use it strategically. Here's how the Kano Model can guide your approach: 1️⃣ Start with the Basics: "Must-Have" AI Today, simply using Gen AI tools is becoming a baseline expectation. Customers already assume you're leveraging these tools for faster responses, content creation, or data analysis. If you're not here yet, you're already playing catch-up. 2️⃣ Level Up: "Performance-Driven" AI This is where you stand out. By tailoring Gen AI to your business feeding it your data, refining outputs for your audience, or integrating it into workflows you turn a generic tool into a competitive edge. Think smarter chatbots, hyper-relevant marketing, or real-time analytics. 3️⃣ The Magic Moment: "Delightful" AI Here's where you surprise people. Imagine AI that anticipates needs before customers ask, adapts in real-time based on behavior, or creates entirely new experiences. Think self-improving systems or creative solutions that redefine what's possible. This isn't just "innovation" it's future-proofing. Why This Matters Gen AI isn't a trend it's a tidal wave. Companies that treat it as a checkbox or wait for others to innovate ("We use ChatGPT!") will stagnate. Those who reimagine processes, products, and customer journeys around AI will lead their industries. The risk? Waiting too long. Early adopters aren't just gaining efficiency they're shaping expectations. Falling behind could mean playing an endless game of catch-up. My Challenge to You Start small, but think big. Master the basics, then aim for differentiation. And always ask: "How could AI not just meet but redefine what's possible here?" I've seen firsthand how this framework drives real impact. What do you think? Could the Kano Model shape your AI strategy? Let's chat in the comments! 👇 (P.S. If you're stuck at "Where do I even start?", let me know happy to share practical steps)

I use these 6 Claude Code features on repeat. The last one is my favorite. 1. Auto Mode. Claude works autonomously while you're away. I turned it on, went to get coffee, came back to a complete analysis of 76 pages of source material. 5 agents had run in parallel, extracted every usable data point, and compiled it into a structured document. The human bottleneck isn't thinking. It's sitting there watching AI work. 2. Custom Skills. You can build reusable commands. I built one called /write-exec-intro. One slash command, and Claude researches the target company's annual report, maps regulations to our product, checks competitors, and writes a board-level introduction message. What used to take my team a full day runs in minutes. And it gets better every time I refine the skill. 3. MCP Servers.                                                                                                     Claude can connect directly to external tools. I connected Notion. Now I tell Claude to pull last week's meeting notes, and it reads them live. Then I run one of our skills /build-presentation. Claude takes the raw notes and builds a fully designed slide deck. From Notion to (almost 😉) finished presentation without me touching anything in between.  4. Parallel Agents. Claude can launch multiple agents at once. I use this for research. Here is an example for sales 7 agents running simultaneously: one on the annual report, one on regulatory mapping, one on competitive landscape, one on communication. The output is deeper than what most sales teams produce in a week. 5. Screenshot Reading. I built my personal website by giving it one screenshot of a cool website as a design reference. Claude looked at it, built a first version, then screenshotted its own output using Playwright, compared the two, and iterated until it is extremely close to the input. I didn't touch anything. 7 rounds of visual feedback with zero input from me. That's what you want AI to do generally -> create loops where AI iterates on its own input without your interference. These are examples, you can use the logic for any work you touch. Is there any workflow I should create a step-by-step guide for? #automation #AI #claudecode #anthropic