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29 articles

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May 15, 2026 · Artificial Intelligence

Five Intent Recognition Designs: From Keyword Matching to Classifier to LLM Self‑Routing – A Decision Tree to Choose the Right One

The article breaks down five production‑grade intent‑recognition designs—keyword matching, regex‑rule engine, embedding classifier, fine‑tuned small model, and zero‑shot LLM routing—provides code snippets, latency and cost benchmarks, decision‑making rules, and shows how a layered architecture can cut API costs from ¥80,000 to ¥3,000 while keeping accuracy above 90%.

LLM routingembedding classifierfine‑tune model

0 likes · 16 min read

Five Intent Recognition Designs: From Keyword Matching to Classifier to LLM Self‑Routing – A Decision Tree to Choose the Right One

Wu Shixiong's Large Model Academy

Apr 27, 2026 · Artificial Intelligence

Can Your RAG Pass the Demo? Scaling to 5,000 Docs for Reliable Answers

The article walks through the practical challenges of turning a RAG demo into a production system for 5,000 insurance documents, covering knowledge‑base chunking, embedding model selection, recall‑threshold tuning, hybrid vector‑BM25 retrieval, intent‑aware query routing, prompt constraints, confidence scoring, and operational scaling, with concrete metrics and code examples.

EmbeddingHybrid RetrievalPrompt engineering

0 likes · 16 min read

Can Your RAG Pass the Demo? Scaling to 5,000 Docs for Reliable Answers

Instant Consumer Technology Team

Jan 13, 2026 · Artificial Intelligence

Scalable Enterprise AI Assistant: Intent Planning, Context Engineering, Data Iteration

This article details the end‑to‑end design of an enterprise AI office assistant, covering the three‑layer framework of intent planning, context engineering, and data self‑iteration, the key pain points of intent understanding, knowledge integration, and quality control, and practical architectural and implementation solutions for scalable deployment.

AI AssistantAgent CollaborationContext Engineering

0 likes · 25 min read

Scalable Enterprise AI Assistant: Intent Planning, Context Engineering, Data Iteration

PMTalk Product Manager Community

Jan 7, 2026 · Artificial Intelligence

Why AI+AR Product Managers Who Fuse Sensor Data and User Behavior Are In High Demand

The article analyses AI‑augmented reality intent‑recognition systems, detailing multi‑modal data fusion, three‑way interaction architectures, and adaptive response mechanisms, and demonstrates their impact across medical surgery, elderly care, accessibility, and product design while outlining technical challenges and design methodologies.

AIARHuman-Computer Interaction

0 likes · 20 min read

Why AI+AR Product Managers Who Fuse Sensor Data and User Behavior Are In High Demand

IT Services Circle

Dec 7, 2025 · Artificial Intelligence

Why Function Calling Fails in AI Agents and How to Fix It

The article examines the three main challenges of complex AI projects—knowledge organization, data‑AI interaction, and intent recognition—highlighting why tool (function) calling often breaks, and presents practical engineering strategies such as prompt refinement, clearer tool schemas, intent convergence, and evaluation loops to improve reliability.

AI agentsData FlywheelFunction Calling

0 likes · 12 min read

Why Function Calling Fails in AI Agents and How to Fix It

Ele.me Technology

Nov 13, 2025 · Artificial Intelligence

How Multi‑Agent AI Architecture Solves Complex Data Generation Challenges

This article details the design and evolution of a multi‑agent AI system for automated data generation in integration testing, covering challenges, single‑ versus multi‑agent approaches, prompt engineering, tool governance, intent recognition, tool filtering, reasoning execution, performance gains, and practical recommendations.

AIData GenerationMulti-Agent

0 likes · 25 min read

How Multi‑Agent AI Architecture Solves Complex Data Generation Challenges

Alibaba Cloud Developer

Oct 17, 2025 · Artificial Intelligence

Unlocking Precise AI Data Generation with Multi‑Agent Architecture

This article explains how a multi‑agent system—comprising intent‑recognition, tool‑engine, and inference agents—solves the challenges of AI‑driven data generation (AI‑造数) by improving accuracy, speed, and scalability through modular design, prompt engineering, and sophisticated tool governance.

AIData GenerationMulti-Agent

0 likes · 24 min read

Unlocking Precise AI Data Generation with Multi‑Agent Architecture

AI Code to Success

Mar 6, 2025 · Artificial Intelligence

Inside the Brain Module: How AI Agents Process, Remember, and Decide

This article provides a comprehensive analysis of the Brain module in AI agents, covering its multi‑step workflow, knowledge integration, memory mechanisms, intent recognition, planning strategies, reasoning techniques, and the role of reflection and emotion in enhancing adaptability and robustness.

Brain moduleEmotion simulationMemory Architecture

0 likes · 17 min read

Inside the Brain Module: How AI Agents Process, Remember, and Decide

Tencent Docs Tech Team

Nov 13, 2024 · Artificial Intelligence

Technical Architecture and Practices of the AI Document Assistant

This article explores the challenges large language models bring to efficiency tools, outlines the AI document assistant's technical thinking and architecture, and details both application‑side and model‑side practices such as retrieval‑augmented generation, intent recognition, and code‑driven table handling, concluding with key lessons.

AIAI ArchitectureDocument Automation

0 likes · 16 min read

Technical Architecture and Practices of the AI Document Assistant

AsiaInfo Technology: New Tech Exploration

Sep 23, 2024 · Artificial Intelligence

How Large Language Models Power Multi‑Turn Dialogue for Smart Marketing

This article presents a comprehensive technical analysis of using large language models to build a task‑oriented multi‑turn dialogue system for intelligent marketing, detailing architecture, intent detection, slot extraction, prompt design, dialogue management, practical experience, and future research directions.

LLMintelligent marketingintent recognition

0 likes · 21 min read

How Large Language Models Power Multi‑Turn Dialogue for Smart Marketing

Meituan Technology Team

Nov 23, 2023 · Frontend Development

Multimodal UI Interaction Intent Recognition for Automated Front‑End Testing

Meituan’s in‑store platform team and Prof. Zhou Yangfan’s Fudan group built a lightweight multimodal UI interaction intent recognizer that fuses screenshots, visible text, and render‑tree attributes via a Vision‑Transformer and Chinese BERT self‑attention model, then clusters nodes with a supervised pairwise classifier, achieving the highest F1 scores among baselines on 158 annotated order‑page screenshots from four business lines, enabling automatically generated test cases that execute correctly on 89 % of 100 unseen pages and demonstrating robust, cross‑app generalization for large‑scale front‑end quality assurance.

UI testingfrontend qualityintent recognition

0 likes · 27 min read

Multimodal UI Interaction Intent Recognition for Automated Front‑End Testing

HelloTech

Sep 13, 2023 · Artificial Intelligence

AI Platform‑Powered Automated Ticket Routing: Modeling Workflow, Feature Engineering, and Intent Recognition

The Haro AI platform automates customer‑service ticket routing by applying a four‑step pipeline—feature processing, model training, evaluation, and deployment—using BERT/ALBERT‑based intent recognition, configurable feature storage, AutoML or expert modes, and Faas‑style deployment, as demonstrated in the Universal Ticket System case study, dramatically improving accuracy and efficiency.

AI PlatformALBERTBERT

0 likes · 11 min read

AI Platform‑Powered Automated Ticket Routing: Modeling Workflow, Feature Engineering, and Intent Recognition

DataFunSummit

Nov 20, 2022 · Artificial Intelligence

NLP Technology Applications and Research in Voice Assistants

This article presents an in‑depth overview of NLP techniques used in voice assistants, covering the end‑to‑end conversational AI pipeline, intent and slot modeling, multi‑turn dialog management, model deployment pipelines, quantization methods, and self‑learning strategies for continuous improvement.

Conversational AIModel QuantizationNLP

0 likes · 30 min read

NLP Technology Applications and Research in Voice Assistants

Ctrip Technology

Nov 10, 2022 · Artificial Intelligence

Improving Search Intent Recognition and Term Weighting with Deep Learning and Model Distillation at Ctrip

This article describes how Ctrip's R&D team applied deep‑learning models, BERT‑based embeddings, knowledge distillation, and term‑weighting techniques to enhance e‑commerce search intent recognition and term importance estimation, achieving high accuracy while meeting sub‑10 ms latency requirements.

BERTDeep LearningSearch

0 likes · 12 min read

Improving Search Intent Recognition and Term Weighting with Deep Learning and Model Distillation at Ctrip

DataFunTalk

Mar 17, 2022 · Artificial Intelligence

A Survey of Text Classification and Intent Recognition: Industrial and Research Perspectives

This article reviews recent developments in text classification and intent recognition, comparing industrial practices such as business‑coupled feature engineering with research trends like pretrained language models, and provides references and practical insights for building effective NLP solutions.

NLPindustry applicationsintent recognition

0 likes · 13 min read

A Survey of Text Classification and Intent Recognition: Industrial and Research Perspectives

DataFunTalk

Dec 16, 2021 · Artificial Intelligence

OPPO XiaoBu Assistant: Building a Low‑Code, End‑to‑End Dialogue System Platform

This article presents OPPO's XiaoBu Assistant platform, detailing its low‑code workflow for business domain modeling, multi‑type NLU (model‑based, retrieval‑based, and QA), componentized core services, large‑scale text processing, vector retrieval, and flexible dialogue management that together enable a complete skill lifecycle from development to online optimization.

AILow‑code platformNLU

0 likes · 18 min read

OPPO XiaoBu Assistant: Building a Low‑Code, End‑to‑End Dialogue System Platform

58 Tech

Nov 18, 2021 · Artificial Intelligence

Intelligent Search Strategy for 58 Recruitment: Breaking Category Constraints and Building a Smart Recall Framework

This article describes how 58 recruitment revamped its search system by removing rigid category limits, introducing query rewriting, intent recognition, doc understanding, and vector‑based recall, resulting in significantly higher relevance, reduced bad cases, and improved commercial performance.

AIQuery RewritingSearch

0 likes · 14 min read

Intelligent Search Strategy for 58 Recruitment: Breaking Category Constraints and Building a Smart Recall Framework

Xianyu Technology

Aug 4, 2021 · Artificial Intelligence

Design and Impact of a Chat Assistant for E‑commerce Negotiation and Greeting

The study presents an asynchronous chat‑assistant for second‑hand e‑commerce that uses intent recognition, price‑strength data, and filtered messaging to generate friendly bargaining and greeting scripts, achieving a 4 % rise in reply and conversion rates, and outlines plans to add more intents and richer product extraction.

Chatbotconversation AIe‑commerce

0 likes · 8 min read

Design and Impact of a Chat Assistant for E‑commerce Negotiation and Greeting

Zhengtong Technical Team

Jun 5, 2020 · Artificial Intelligence

Design and Implementation of an Intelligent Chatbot System: Intent Recognition Algorithms and Architecture

The article details the architecture and intent recognition mechanisms of an AI chatbot for urban management, exploring regex matching, Levenshtein distance, and Naive Bayes classification, alongside dynamic model training and frontend data rendering strategies.

AI System DesignChatbot ArchitectureLevenshtein distance

0 likes · 11 min read

Design and Implementation of an Intelligent Chatbot System: Intent Recognition Algorithms and Architecture

HomeTech

Nov 20, 2019 · Artificial Intelligence

Query Understanding and Intent Recognition in Search: Methods, Taxonomy, and Applications

This article explains how query understanding (QP) transforms user search queries into structured semantic blocks and intent categories using rule‑based NLP, entity recognition, and post‑processing, and describes its taxonomy, implementation details, and practical impact on search engine results.

Knowledge GraphNLPQuery Understanding

0 likes · 16 min read

Query Understanding and Intent Recognition in Search: Methods, Taxonomy, and Applications

Ctrip Technology

Nov 7, 2019 · Artificial Intelligence

Intelligent Customer Service in Travel: System Architecture and Key Technologies

This article explains the architecture and core technologies of Ctrip’s intelligent travel customer service, covering NLU, dialogue state tracking, policy learning, intent and slot extraction, multi‑round task bots, and the supporting platform for deployment and future multimodal extensions.

AIChatbotNLU

0 likes · 12 min read

Intelligent Customer Service in Travel: System Architecture and Key Technologies

58 Tech

Oct 16, 2019 · Artificial Intelligence

Design and Implementation of Intent Recognition, Semantic Similarity Matching, and Slot Filling for a Voice Robot

This article details the architecture and algorithms behind a voice robot's natural language understanding module, covering single‑sentence intent classification with TextCNN, acoustic quality detection using VGGish‑BiLSTM, semantic similarity matching via DSSM and TextCNN‑Transformer, and slot‑filling with IDCNN‑CRF, along with performance results and future directions.

AINLUTextCNN

0 likes · 11 min read

Design and Implementation of Intent Recognition, Semantic Similarity Matching, and Slot Filling for a Voice Robot

Qunar Tech Salon

Oct 10, 2019 · Artificial Intelligence

Intelligent Customer Service System for Airline Ticket Business: Architecture, Data Analysis, and AI Techniques

This article describes the design and implementation of an AI‑powered intelligent customer service system for airline ticket operations, covering data‑driven problem analysis, dialogue architecture, intent recognition using BERT and fastText, knowledge‑base QA, and future development plans.

AIBERTIntelligent Customer Service

0 likes · 11 min read

Intelligent Customer Service System for Airline Ticket Business: Architecture, Data Analysis, and AI Techniques

Xianyu Technology

Aug 20, 2019 · Artificial Intelligence

How Xianyu Boosted Sales with an AI-Powered Auto-Reply Chatbot

Xianyu tackled slow seller responses, inconsistent answers, and bargaining friction by building an AI-driven auto-reply system that extracts product attributes, generates knowledge bases, and uses intent recognition and response generation models, resulting in faster replies, higher conversion rates, and reduced seller workload.

AIAlibabaChatbot

0 likes · 12 min read

How Xianyu Boosted Sales with an AI-Powered Auto-Reply Chatbot

DataFunTalk

May 23, 2019 · Artificial Intelligence

AI Techniques in Xiaomi Mobile Search: Text Relevance, Intent Recognition, and Click‑Model Ranking

The article presents Xiaomi's mobile search system, detailing how AI methods such as deep learning, GBDT and DNN models are applied to text relevance calculation, intent detection with term‑weighting, and click‑through ranking models (PBM, Cascade, DBN) to improve user experience across heterogeneous result types.

AISearchclick models

0 likes · 9 min read

AI Techniques in Xiaomi Mobile Search: Text Relevance, Intent Recognition, and Click‑Model Ranking

JD Tech

Jan 16, 2019 · Artificial Intelligence

Technical Deep Dive of JD’s Intelligent Customer Service 2.0: AI‑Driven Intent Recognition, Emotion Analysis, and Smart Scheduling

This article presents a comprehensive technical analysis of JD’s Intelligent Customer Service 2.0, detailing AI‑based intent recognition with the ABSQ framework, hierarchical attention networks, emotion analysis via CNN, speech navigation using ASR/NLP, and machine‑learning‑driven smart dispatch that together boost accuracy and user experience.

AIcustomer-serviceemotion analysis

0 likes · 10 min read

Technical Deep Dive of JD’s Intelligent Customer Service 2.0: AI‑Driven Intent Recognition, Emotion Analysis, and Smart Scheduling

iQIYI Technical Product Team

Sep 14, 2018 · Artificial Intelligence

Limitations of Language Models in Voice Interaction and HomeAI Solutions

iQIYI HomeAI tackles the bottleneck of static language models in voice assistants by separating phonetic and semantic processing, correcting ASR errors at the intent‑recognition layer with pinyin‑enhanced entity correction, thereby reducing error amplification in video‑on‑demand interactions and paving the way for adaptive, personalized voice experiences.

AILanguage Modelintent recognition

0 likes · 7 min read

Limitations of Language Models in Voice Interaction and HomeAI Solutions

DataFunTalk

Apr 18, 2018 · Artificial Intelligence

Introduction to Search Engine Algorithm Systems: Ranking and Intent Recognition

This article provides a comprehensive overview of search engine algorithm systems, tracing their evolution from simple Bayesian and SVM models to modern deep learning approaches, and detailing the architecture, query analysis, ranking methods, click models, and recent advances such as reinforcement learning and adversarial networks.

AILTRclick models

0 likes · 13 min read

Introduction to Search Engine Algorithm Systems: Ranking and Intent Recognition

Qunar Tech Salon

Jul 10, 2017 · Artificial Intelligence

Qunar Intelligent Service Robot: Architecture, Cognitive System, and Iterative Development

The article details Qunar's development of an AI-powered customer service robot, describing its motivation, data analysis, multi‑phase cognitive system architecture, knowledge‑base management, evaluation mechanisms, and future integration into a group‑wide intelligent service platform to improve service efficiency and reduce costs.

AIChatbotDeep Learning

0 likes · 17 min read

Qunar Intelligent Service Robot: Architecture, Cognitive System, and Iterative Development