Today I improved Nexa AI’s agent system. What I worked on: ✅ Career matching improvements ✅ Skill gap analysis ✅ Job readiness recommendations ✅ Personalized learning roadmaps ✅ Career-based project suggestions Nexa AI can now analyze a user profile and provide a more personalized career plan instead of giving generic advice. Continuing to improve the agent workflow and make Nexa smarter every day! 🔥 #AgenticAI #AIChallenge

Yesterday's Decoding Data Science session was another reminder that building AI starts with solving the right problem—not just using AI because it's available. One of my biggest takeaways was learning how to identify the right AI solution: ✅ Automation for repetitive tasks ✅ LLMs for understanding and generating unstructured content ✅ RAG when knowledge retrieval is the challenge ✅ AI Agents for multi-step workflows and decision-making I also learnt about the 6-Layer AI Application Stack, which provides a practical roadmap for building AI applications: 1️⃣ Problem & Use Case 2️⃣ Data Layer 3️⃣ Intelligence Layer 4️⃣ Workflow Layer 5️⃣ Interface Layer 6️⃣ Evaluation Layer This session helped me understand that great AI products are built by carefully designing every layer—not by simply connecting to an LLM. Thank you, Mohammad Arshad and the Decoding Data Science community, for another valuable learning experience. Every session continues to strengthen my understanding of AI engineering and how to build solutions that solve real-world problems. Looking forward to applying these concepts in my own AI projects! #DecodingDataScience #ArtificialIntelligence #AIEngineering #LLM #RAG #AIAgents #Automation #MachineLearning #BuildInPublic #ContinuousLearning

I had the privilege of attending a recent session with DDS AI and learned so much about thinking about your product when working with AI, Agentic AI vs RAG, Prompt Engineering, and using the appropriate language model for your use case. One major insight was that when building with AI you want to start with a real problem you are trying to solve instead of picking the latest and greatest model or trying to make another chatbot. We discussed how Agentic AI is capable of reasoning, using tools, having a memory, and acting on a plan. While RAG allows your AI to connect with verified documents to create more grounded answers. I’m also learning the hard way how important it is to just try things out, be your own user when testing, write down what works, and iterate. Just because one model performed better for one task doesn’t mean they will for another task. Select your model based on what the user needs and what your project requires. I’m an Electrical Engineering student, and these are lessons I will take with me as I start to brainstorm for my senior design project. I’ve been working on an idea for a while now. It’s an AI called CampusMate, an intelligent university assistant to help students keep track of classes, search academic documents, and plan their semester. Thanks to Decoding Data Science and the DDS Business Circle, the mentors that lead the sessions, and the AI community as a whole. I can’t wait to learn more, try new things, build, reflect on my successes and failures, and share my journey. #DDSNextGen #DDSAmbassador #DecodingDataScience #DDSBusinessCircle #AgenticAI #ArtificialIntelligence

Today I continued improving Nexa AI, my AI career assistant project. Instead of only adding features, I focused on making sure the agent workflow is clear and useful. Today I worked on: ✅ Reviewing the role of each agent ✅ Planning how Nexa can support different career backgrounds ✅ Preparing demo scenarios ✅ Organizing the next steps for the project Building AI systems is not just about the model — it’s about creating a workflow that can understand users and provide meaningful results. Looking forward to continuing the development! 🚀 #AgenticAI #AIProjects

Over the past two days, I’ve been diving deep into AI product development and Retrieval-Augmented Generation (RAG)—gaining both strategic frameworks and hands-on experience building real-world AI solutions. 🚀 Workshop 1: AI Product Thinking & RAG Foundations The opening session focused on the bridge between business requirements and technical execution—understanding how successful AI products are actually designed and built. Key Learnings: Idea Translation: Converting raw concepts into clearly defined AI project scopes. Problem-First Mindset: Identifying core business challenges before picking the tech stack. Data Strategy: Defining data requirements and effective ingestion strategies. LLM Evaluation: Comparing model capabilities, latency, and cost structures across different providers to select the best fit. Parameter Tuning: Mastering OpenAI configuration parameters like Temperature, Top-P, and Max Tokens. RAG Architecture: Understanding the end-to-end workflow 🤖 Workshop 2: Building a RAG Chatbot with LlamaIndex The second workshop was all about hands-on implementation in Google Colab, building a functional RAG pipeline from scratch. Tech Stack: ⚙️ LlamaIndex: Document ingestion, chunking, indexing, retrieval orchestration, and context management. ⚙️ Gradio: Rapid deployment of a clean, interactive chatbot user interface. Key Takeaway: LlamaIndex simplifies complex RAG engineering tasks that would otherwise require extensive custom boilerplate. It allows developers to focus on solving core business problems rather than managing underlying infrastructure. 💡 Practical Project: DDS HR Chatbot To apply these concepts directly, I built an HR Chatbot for DDS using the RAG architecture. Key Features & Impact: Retrieves answers directly from internal HR documents. Delivers accurate, context-aware responses. Minimizes hallucinations by grounding answers strictly in company knowledge. Demonstrates how domain-specific AI can transform internal knowledge access and employee support.