8-Chems

Implementation Blueprint Catalog Fact-Checking & Confidence Pipeline Overview The Problem Architecture GCP Project Structure Review Interface KPI Dashboards Tech Stack Phased Plan Risks Retail & E-commerce · LLM Evaluation Every LLM-generated catalog claim gets a confidence score before it reaches a customer. An autonomous fact-checking layer that validates LLM-generated product descriptions, attributes, and personalization insights against a structured knowledge graph — catching bad pricing, mismatched attributes, and broken personalization before publication. 4 Validation signals fused 0–1 Per-claim confidence score 3 Routing outcomes GCP Target deployment cloud Why This Exists LLMs write fluent catalog copy. Fluent isn't the same as correct. Once LLMs started generating product descriptions, attributes,...

Generative Recommenders: Foundations, Engineering, and a Path to Production Machine Learning · Recommender Systems Generative Recommenders: Foundations, Engineering, and a Path to Production How Hierarchical Sequential Transduction Units (HSTU) turn recommendation into sequential transduction — trillion-parameter models, 15x faster training, up to 285x more FLOPs served at similar latency — and what it takes to actually run this in production. 1.5T params in production GR 12.4% online metric lift (E-Task) 15.2x faster training vs. FlashAttention-2 Transformers 285x more FLOPs served at 1.5–3x QPS Most recommendation systems in production today are Deep Learning Recommendation Models — DLRMs. They are the workhorse behind a decade of progress: YouTube DNN, Wide&Deep, DIN, DCN, DHEN. And they share a quiet, structural problem: they scale with data, not with compute. Throw more GPUs at a DLRM and, past a point, nothing happens. Quality plateaus. The pape...