ADAmoy Duttainblogs-amoydutta.hashnode.dev·8h ago · 18 min readCan Jev Actually Sort Your IT Tickets? We Built a Baseline and Found OutTL;DR We wanted an honest answer to a simple question: is a typed, calibrated judgment model ("System One") like TypeSafe AI's Jev genuinely good or is it just an LLM with better marketing? So we buil00
ADAmoy Duttainblogs-amoydutta.hashnode.dev·Feb 10 · 8 min readExplainable Recommendations in Practice: A Demo-Driven ApproachThis blog complements my earlier post on AI-based explainability (xAI) for recommendation systems and the corresponding Movie Recommendations App. While demo apps should ideally speak for themselves, explainable systems often benefit from guided cont...00
ADAmoy Duttainblogs-amoydutta.hashnode.dev·Jan 26 · 8 min readThe End of the Black Box: Why the "Why" is the Future of PersonalizationIf you spend five minutes in a Product Management circle today, the word "Personalization" will likely be the most overused term in the room. We treat recommendation engines as the silent engines of growth, the invisible hands that guide users throug...00
ADAmoy Duttainblogs-amoydutta.hashnode.dev·Dec 22, 2025 · 11 min readBuilding a Smarter Product Chatbot: What RAG Gets Right—and Where It BreaksImagine you're on a vast electronics e-commerce site like samsung.com. You're surrounded by hundreds of products across dozens of categories. You have specific needs- "a durable camera for hiking" or "a quiet laptop for a student" - but the website's...00
ADAmoy Duttainblogs-amoydutta.hashnode.dev·Nov 4, 2025 · 7 min readBreaking Down RAG: Why Chunking Can Make or Break Your LLMTL;DR:Retrieval-Augmented Generation (RAG) helps large language models (LLMs) like GPT-5 and Claude 4.5 access private, real-time, or domain-specific data by retrieving relevant chunks before generating answers. This article breaks down what RAG is, ...00
