We’re quietly launching the DDS Builder Codex for our Skool community. It is your starting roadmap to understand the four stages of your AI journey: Identity, Learning, Building and Community. I invite our community members to complete the Codex, follow the instructions provided inside, and share your learning journey with the community. Your feedback will help us improve the experience before the wider launch. Start here: https://www.skool.com/decoding-data-science-6929/classroom/740bbfa7

Receiving this winner’s certificate is a proud milestone in my journey of developing ProSightAI into a fully functional, secure, and responsible Agentic AI application. A huge thank you to Decoding Data Science, the organizers, mentors, judges, and everyone in this community for the guidance, encouragement, and opportunity to learn and build alongside other AI enthusiasts. This achievement motivates me to keep improving ProSightAI, strengthen its agentic and security capabilities, and turn it into an even more impactful solution. Grateful for the journey—and excited for what comes next! 🚀🤖

What is the best way to build a documentation for the App that we have just built and how can the versioning be maintained for the same Would love to get suggestions and feedback from the experienced and senior members Thanks

🏙️ I built a live AI product in 3 days. Here's what it does and how it works. Introducing DubaiNest AI — a RAG-powered real estate assistant for Dubai, built by own from scratch during the AI Accelerator Bootcamp learning by Decoding Data Science. The problem it solves: Every expat in Dubai knows this frustration — scattered rental prices, confusing RERA laws, no single place to get a straight answer. DubaiNest AI changes that. You can ask it: 🔹 "What is the average rent for a 1BR in JVC?" 🔹 "Can my landlord increase rent by 20%?" 🔹 "What is the total move-in cost for an AED 90,000 flat?" 🔹 "Which areas suit a young professional?" And it answers accurately — grounded in real data, no hallucination. The tech stack: ⚙️ LlamaIndex — RAG pipeline & query engine 📦 Pinecone — cloud vector database (1536-dim embeddings) 🤖 OpenAI GPT-4o-mini — LLM (temperature=0, factual answers) 🌐 Flask + Waitress — production API server 🐳 Docker — containerised deployment 🤗 HuggingFace Spaces — live hosting, single URL What I learned building this: ✅ Data quality matters more than model choice ✅ LlamaIndex's {context_str}/{query_str} != LangChain's {context}/{question} — a small difference that breaks everything ✅ Shipping a real product is completely different from running a notebook I am a Mechanical Automation & Maintenance Engineer now specialising in Industrial AI. Most software people build AI apps. I build AI apps that understand real physical systems and real operational problems. This is what 3 days of focused building looks like. 👇 🔗 Try it live: https://lnkd.in/dAFBcYM5 💻 GitHub: https://lnkd.in/d9cGAUcp Mohammad Arshad Bayut.com dubizzle Property Finder Dubai Land Department Emaar DAMAC Properties Better Home Group

Over the past two days, I've been diving deep into AI product development and Retrieval-Augmented Generation (RAG), gaining both strategic understanding and hands-on experience building real-world AI solutions. 🚀 Workshop 1: AI Product Thinking & RAG Foundations The first session focused on understanding how successful AI products are built—from idea to implementation. Key learnings: ✅ Converting raw ideas into clearly defined AI projects ✅ Identifying real business problems before selecting technologies ✅ Understanding data requirements and collection strategies ✅ Evaluating Large Language Models (LLMs) for different use cases ✅ Comparing model capabilities and costs across providers ✅ Selecting the most suitable model for business needs ✅ Working with OpenAI parameters such as Temperature, Top-P, and Max Tokens ✅ Understanding the complete RAG workflow: Documents → Retrieval → Knowledge Base → Response Generation This session completely changed how I view AI product development by connecting business requirements with technical implementation. 🤖 Workshop 2: Building a RAG Chatbot with LlamaIndex & Pinecone The second workshop was highly practical. Using Google Colab, I built a functional RAG-based chatbot from scratch. Technologies used: ⚙️ LlamaIndex – Document ingestion, chunking, indexing, retrieval orchestration, and context management ⚙️ Pinecone – Vector database for storing and retrieving embeddings ⚙️ Gradio – Rapid development of an interactive chatbot interface One of the biggest takeaways was understanding the power of LlamaIndex. It simplifies many complex RAG engineering tasks that would otherwise require significant custom development, allowing developers to focus more on solving business problems rather than infrastructure challenges. 💡 Practical Project: DDS HR Chatbot As part of the workshop, I developed an HR Chatbot for DDS using a RAG architecture. The chatbot: 🔹 Retrieves information directly from internal HR documents 🔹 Provides context-aware responses