Wall of love for Building Agentic AI Applications with a Problem-First Approach

Excited to share that I've completed the "Building Generative AI Applications with a Problem-First Approach" certification from Maven! 🎓 This cohort-based course taught me how to design and build generative AI solutions that solve real-world challenges with a focus on impact rather than just following trends. I gained valuable insights into creating agentic AI applications using a problem-first methodology. Special thanks to the instructors, Aishwarya Naresh Reganti and Kiriti Badam, for their excellent guidance throughout this learning journey. Looking forward to applying these skills in developing AI solutions that address meaningful problems! #GenerativeAI #AIcertification #ProfessionalDevelopment #Maven #ContinuousLearning #AIskills

Just Completed an Amazing AI Course! 🚀 I'm thrilled to share that I've just wrapped up an intensive 6-week course (not gonna lie...it nearly broke me) on building agentic AI systems with a problem-first approach. Led by Aishwarya Naresh Reganti and Kiriti Badam and this is where you can sign up for the next cohort (it's worth 10x the cost): lnkd.in/g-km_TDM The fifteen capstone project presentations were mind-blowing - here are five that really stood out: 🚀 AI-Powered Medical Documentation A team built a system that transcribes doctor-patient conversations, structures them into proper medical notes, suggests medical codes, and even manages workflow tasks - potentially saving physicians 19-20 minutes per patient! The working prototype showed how AI could give doctors more time with patients instead of paperwork. 🚀 Loan Underwriting Assistant This elegant solution addressed the inefficiencies in consumer loan assessments, using historical data clustering and a multi-agent system to evaluate creditworthiness while maintaining regulatory compliance. I loved how they progressively reduced human intervention while still keeping humans in the loop for borderline cases. 🚀 Inverter Maintenance AI The Hitachi team tackled the problem of navigating 3000+ page technical manuals with a dual-agent system - one for maintenance expertise and another dedicated to safety. Their thoughtful approach to industrial safety made this stand out. 🚀 Wildlife Conservation AI Using AI to process millions of camera trap images and GPS tracking data from lions in Kenya showed how these technologies can support conservation efforts. The careful consideration of cost constraints while maintaining accuracy was impressive. 🚀 Digital Habit Coach Based on behavioral psychology frameworks like Atomic Habits, this system helps users form lasting habits through personalized plans and adaptive check-ins. The multi-agent architecture that provides progressively personalized coaching showed AI's potential beyond enterprise applications. and there were so many more, including ours 🚀 Account Intelligence Platform Unlike standard tools that just provide generic company information, we built a solution that maps target company data against your own product portfolio to identify specific sales opportunities. What impressed me most was how each team applied an iterative approach, starting with simple solutions before adding complexity - a reminder that solid AI implementation isn't about using the most advanced techniques, but about solving real problems effectively. The future of AI is less about hype and more about thoughtful application to meaningful problems. It’s fascinating and exhilarating building at the edge of rapidly changing technology and taking the time for technical learning helps us at agentiiv refine our thesis for what’s next. 🚀

🚀 Just wrapped up the incredible GenAI System Design course with Aishwarya Naresh Reganti and Kiriti Badam on Maven. This wasn't just another theoretical overview; it was a deep dive into the practicalities of building effective and scalable GenAI applications. One of the biggest lessons? 🏗️ 𝗦𝘁𝗮𝗿𝘁 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝗱 𝘃𝗲𝗿𝘁𝗶𝗰𝗮𝗹𝗹𝘆, not horizontally. Emphasized an iterative approach, reminding us that the true power of GenAI lies not just in generation, but in understanding the context we provide. Here are the main takeaways: 👑 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗞𝗶𝗻𝗴: The real challenge isn't always about finding the perfect model, they will become commodities but providing the right context. We dove deep into Retrieval-Augmented Generation (RAG) techniques to address this head-on. 🚀 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗳𝗼𝗿 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Learned practical strategies to save costs and improve performance, including semantic caching and various chunking strategies. 🧠 𝗕𝗲𝘆𝗼𝗻𝗱 𝗕𝗮𝘀𝗶𝗰 𝗥𝗔𝗚: Explored advanced concepts like Graph RAG and Corrective RAG approaches to enhance the accuracy and reliability of GenAI systems. 🤖 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘃𝘀. 𝗔𝗴𝗲𝗻𝘁𝘀: Gained a clear understanding of when to use a simple AI workflow versus a more complex agent-based approach. 👁️ 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 is KEY for agents due to their non-deterministic nature. 🎭 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗠𝗮𝗴𝗶𝗰: Learned the criteria for employing a multi-agent system: only when a single agent demonstrably cannot solve the problem. 💰⏱️ 𝗖𝗼𝘀𝘁/𝗟𝗮𝘁𝗲𝗻𝗰𝘆 & 🛡️ 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗮𝗿𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹: Always evaluate your cost and latency implications when building GenAI systems and of course guardrails are critical. Lastly, 🔄 effective design decisions come from an iterative approach. Thank you once again Aishwarya Naresh Reganti & Kiriti Badam for this insightful experience!

🤖The Evolution of Coding: From Pro-Code to No-Code🚀 A new kid on the block - The LangGraph Builder📊 The evolution of software development is nothing short of extraordinary. We've journeyed from the era of meticulous pro-code development to an age where low-code and no-code platforms are rewriting the rules of innovation. Tools like Langflow, Cursor, Windsurf, and now LangGraph Builder are not merely platforms; they are enablers of creativity, collaboration, and accessibility. People who are not coders are now given the magic wand to build with minimal to no coding. The future belongs to tools that prioritize intelligence & efficiency, and to those who embrace these tools. The LangGraph Builder is another powerful low-code tool bridging the gap between visual design and code implementation. If you are like me, a visual person conversant with low-code tools like LangFlow, LangGraph Builder would be a good segway into the coding world. By the way, thanks to Aishwarya Naresh Reganti and Kiriti Badam for introducing me to these wonderful tools (Langflow and Cursor in particular). How can tools like Langflow and LangGraph Builder help PMs? LangGraph Builder is a low-code interface that allows for a seamless transition from whiteboarding to actual code, making it easier for Product Managers (PMs) to collaborate and iterate. However, my personal favorite will always remain Langflow for a quick show-and-tell, or to test my thought process on a use case. By automating the generation of boilerplate code, LangGraph Builder and Langflow allow Product Managers (and advisors like myself) to sketch out the flow/architecture of AI agents based on business use cases/logic and explain the same to developers easily in their language to implement the core logic. Additionally, the pre-defined templates for common AI agent patterns like Retrieval-Augmented Generation (RAG) and agents with tools provide a quick starting point for many use cases, accelerating the brainstorming and development process. So let's explore this new era of development, where the focus shifts from writing every line of code to solving real-world problems with intelligent, efficient, and collaborative tools. Keep an eye out for my posts on Langflow soon. LangGraph Builder UI: lnkd.in/gbUbDaF9 Langflow: langflow.org #AI #LowCode #LangGraph #LangChain #Langflow #DeveloperTools #OpenSource #ProductManagers