Why Context Modeling Could Replace RAG – Insights from DeepVista CEO Jing Conan Wang

In a two‑hour interview, DeepVista CEO Jing Conan Wang explains how his new "context modeling" paradigm addresses the rigidity, lack of personalization, and performance limits of current RAG‑based AI agents, proposing a dual‑model architecture that learns and adapts context dynamically for faster, more accurate results.

AI ArchitectureLLM optimizationPersonalized AI

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Why Context Modeling Could Replace RAG – Insights from DeepVista CEO Jing Conan Wang

58 Tech

Jan 29, 2021 · Artificial Intelligence

Optimization Practices for Business Opportunity Slot Recognition in 58.com Intelligent Customer Service

This article details the background, challenges, architecture, model selection, and future directions of the business‑opportunity slot recognition module used in 58.com’s intelligent customer service, highlighting how regex‑model fusion and IDCNN‑CRF improve entity extraction for phone, WeChat, address, and time slots.

BERTCRFIDCNN

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Optimization Practices for Business Opportunity Slot Recognition in 58.com Intelligent Customer Service

Ctrip Technology

Jun 4, 2020 · Artificial Intelligence

Semantic Matching Models for Travel QA: Deep Learning Techniques, Interaction Models, and Transfer Learning

This article reviews the evolution of semantic matching models for travel question‑answering, covering traditional keyword and probabilistic methods, deep‑learning encoders such as LSTM, CNN, and Transformer, interaction‑based architectures like MatchPyramid and hCNN, as well as transfer‑learning and multilingual extensions to improve practical deployment.

Deep Learningcontext modelingnatural language processing

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Semantic Matching Models for Travel QA: Deep Learning Techniques, Interaction Models, and Transfer Learning