Official account of DaTaobao Technology
The transaction terminal team created an AI‑driven workflow that automatically collects, cleans, classifies, alerts, distributes, attributes, and reviews user feedback, using a four‑step LLM model to ensure controllable, consistent, and explainable sentiment analysis while boosting efficiency and trust.
This article presents the Trustworthy Generation design pattern for Retrieval‑Augmented Generation (RAG) systems, analyzes four root causes of low trustworthiness—retrieval errors, content reliability, pre‑retrieval reasoning mistakes, and model hallucinations—and proposes layered solutions, citation techniques, CRAG and Self‑RAG architectures, guardrails, and practical trade‑offs.
This article records the end‑to‑end practice of using the AI coding tool Cursor to deliver the front‑end of a red‑packet sign‑in page’s "single‑product rebate" feature, covering requirement breakdown, AI‑generated design and code, popup animations, feed insertion, deduplication, debugging, and final verification.
This article presents DeepStock, a deep reinforcement learning framework with policy regularization that integrates classic inventory heuristics, achieving 7% turnover reduction and multi‑million cost savings across millions of SKU‑warehouse pairs in Alibaba's self‑operated ecosystem.
This article proposes an ASR‑driven pipeline that creates high‑quality AI‑reply vs. human‑like reply pairs, trains a rewrite model and a reward model, and uses GRPO reinforcement learning to generate natural, helpful, and less AI‑sounding responses in digital‑human live streaming, achieving 92% accuracy and 97% helpfulness while improving user experience.
This article details a systematic set of optimizations—dual‑layer pre‑fetch, multi‑level caching, multi‑instance tab management, and white‑screen mitigation—that transformed an e‑commerce order list from sluggish and unstable to fast, smooth, and reliable for billions of users.
This article presents an AI‑driven accessibility solution that embeds code‑review and automatic fixing into the development workflow, tackles high‑frequency issues such as text reading, focus management and interaction recognition, achieves over 95% fix rates in both testing and production, and outlines future extensions to native and D2C scenarios.
This article aggregates ten technical case studies from Alibaba, covering AI‑assisted code cleaning, specification‑driven development, multi‑agent front‑end automation, AI coding fundamentals, index optimization, and workflow designs that together illustrate how AI can boost efficiency across backend, frontend, and database engineering.
The article presents five design patterns—Logits Masking, Grammar, Style Transfer, Reverse Neutralization, and Content Optimization—for steering the output of generative AI models, compares their suitable scenarios, advantages, drawbacks, and anti‑patterns, and provides concrete implementation steps, code snippets, and flowcharts to help developers reliably enforce style, format, and compliance constraints.
The article examines how AI engineering builds on, rather than discards, traditional software engineering principles, using the evolution of AI‑driven search at Alibaba to illustrate architectural upgrades that manage uncertainty, integrate context engineering, and combine classic design patterns with new AI‑specific tools.
