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May 4, 2026 · Artificial Intelligence

Engineering and Algorithm Innovations for RAG Engines in Office Applications

This article analyzes the challenges and practical solutions of building a Retrieval‑Augmented Generation (RAG) system for office scenarios, covering background issues, modular architecture, offline and online pipelines, hybrid retrieval, ranking models, knowledge filtering, prompt design, and two‑stage generation techniques.

AIDocument ParsingHybrid Retrieval

0 likes · 22 min read

Engineering and Algorithm Innovations for RAG Engines in Office Applications

DataFunTalk

Apr 26, 2026 · Artificial Intelligence

Building an Enterprise‑Grade RAG 2.0 System: Architecture, Challenges, and Best Practices

This article analyses the practical construction of an enterprise‑level Retrieval‑Augmented Generation (RAG) 2.0 system, covering background issues of large models, a modular architecture, layered offline/online pipelines, hybrid retrieval, ranking strategies, prompt engineering, and deployment insights drawn from China Mobile’s production experience.

Enterprise AIHybrid RetrievalPrompt engineering

0 likes · 22 min read

Building an Enterprise‑Grade RAG 2.0 System: Architecture, Challenges, and Best Practices

DataFunTalk

Apr 15, 2026 · Artificial Intelligence

Building a Production‑Ready RAG System for Enterprise Knowledge Work

This article analyzes the challenges and practical solutions of deploying Retrieval‑Augmented Generation (RAG) in an enterprise office setting, covering background problems, modular architecture, offline and online pipelines, hybrid retrieval, multi‑stage ranking, knowledge filtering, prompt engineering, and model selection to achieve accurate, reliable answers.

Enterprise AIHybrid RetrievalRAG

0 likes · 21 min read

Building a Production‑Ready RAG System for Enterprise Knowledge Work

DataFunTalk

Apr 6, 2026 · Industry Insights

Building a Production-Ready RAG System: Architecture, Challenges, and Best Practices

This article examines the practical challenges of deploying Retrieval‑Augmented Generation (RAG) in enterprise settings, detailing its core components, modular architecture, offline and online pipelines, document parsing, query rewriting, hybrid retrieval, multi‑stage ranking, knowledge filtering, and prompt‑driven generation to achieve accurate, reliable answers.

Enterprise AIHybrid RetrievalKnowledge Filtering

0 likes · 21 min read

Building a Production-Ready RAG System: Architecture, Challenges, and Best Practices

DataFunTalk

Feb 26, 2026 · Artificial Intelligence

How RAG Can Overcome Large‑Model Pitfalls in Enterprise Knowledge Work

This article explains the challenges large language models face in real‑world applications, introduces Retrieval‑Augmented Generation (RAG) as a solution, and details a modular RAG architecture, its components, and practical techniques for document parsing, query rewriting, hybrid retrieval, ranking, and answer generation in an enterprise setting.

Document ParsingLLM deploymentRAG

0 likes · 22 min read

How RAG Can Overcome Large‑Model Pitfalls in Enterprise Knowledge Work

Sohu Tech Products

Jan 28, 2026 · Artificial Intelligence

How OnePiece Brings Context Engineering and Implicit Reasoning to Industrial Ranking

This article details the OnePiece framework, which integrates context engineering, anchor item sequences, and progressive implicit reasoning into generative recommendation systems, achieving significant offline and online performance gains on Shopee Search by enhancing model inference, personalization, and computational efficiency.

Context EngineeringGenerative RecommendationLarge Language Models

0 likes · 13 min read

How OnePiece Brings Context Engineering and Implicit Reasoning to Industrial Ranking

Architect

Jun 10, 2023 · Artificial Intelligence

An Overview of Twitter’s Open‑Source Recommendation System Architecture

Twitter’s recently open‑sourced recommendation system is dissected, covering its overall architecture, graph‑based data and feature engineering, recall pipelines (in‑network and out‑of‑network), coarse and fine ranking models, mixing and re‑ranking stages, as well as the supporting infrastructure and code examples.

Ranking ModelsTwittergraph embedding

0 likes · 16 min read

An Overview of Twitter’s Open‑Source Recommendation System Architecture

DataFunSummit

Mar 24, 2022 · Artificial Intelligence

An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches

This article provides a comprehensive introduction to Learning to Rank (LTR), describing its four major categories—point‑wise, pair‑wise, list‑wise, and generative models—along with typical algorithms such as Wide & Deep, ESMM, RankNet, LambdaRank, LambdaMART, DLCM, and miRNN, and discusses their architectures, loss functions, and practical considerations in advertising and recommendation systems.

Generative ModelsLearning-to-RankPairwise

0 likes · 22 min read

An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches

DataFunTalk

May 31, 2021 · Artificial Intelligence

Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models

This article presents the challenges of travel‑related product search, explains why traditional rule‑based sorting is insufficient, and describes how Alibaba Flypig’s team built a deep‑learning based personalized ranking system—including architecture, model variants, experimental results, and future optimization directions—to improve conversion rates for flight and ticket searches.

AIDeep LearningRanking Models

0 likes · 9 min read

Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models

Alibaba Cloud Developer

Jul 1, 2020 · Artificial Intelligence

Optimizing Search Timeliness: From Feature Extraction to Ranking Models

This article explains the concept of timeliness in search ranking, defines content and demand side metrics such as half‑life and time sensitivity, describes evaluation criteria, outlines feature extraction and labeling pipelines, and details the multi‑stage modeling, recall, and indexing strategies used to improve timely search results.

Ranking Modelsfeature engineeringinformation retrieval

0 likes · 27 min read

Optimizing Search Timeliness: From Feature Extraction to Ranking Models

58 Tech

Apr 1, 2020 · Artificial Intelligence

Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution

This article describes how 58 Tongzhen leverages AI technologies—including data pipelines, feature engineering, various recall and ranking models, and AB‑testing—to build a personalized feed recommendation system for the down‑market, detailing its overall architecture, data sources, model iterations, performance gains, and future directions.

AB testingAIDeep Learning

0 likes · 20 min read

Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution

Qunar Tech Salon

Feb 27, 2020 · Artificial Intelligence

iQIYI Dual‑DNN Ranking Model with Online Knowledge Distillation

This article describes iQIYI’s dual‑DNN ranking architecture that combines a high‑capacity teacher network with a lightweight student network via online knowledge distillation, addressing the trade‑off between model effectiveness and inference efficiency in large‑scale recommendation systems.

CTR predictionOnline LearningRanking Models

0 likes · 12 min read

iQIYI Dual‑DNN Ranking Model with Online Knowledge Distillation

DataFunTalk

Feb 22, 2020 · Artificial Intelligence

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

The article introduces iQIYI's double‑DNN ranking architecture that combines a high‑performance teacher network with a lightweight student network through online knowledge distillation, detailing the evolution of deep learning‑based ranking models, the motivation for model upgrades, training pipelines, and experimental results that demonstrate significant latency reduction and ROI improvement.

Deep LearningOnline LearningRanking Models

0 likes · 13 min read

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

Meituan Technology Team

Jan 10, 2019 · Artificial Intelligence

Deep Learning and Ranking Model Evolution for Hotel Search at Meituan

The talk explains how Meituan transformed its O2O hotel search by layering a multi‑stage retrieval pipeline with intent‑aware NLP, then progressively upgrading ranking—from XGBoost to MLPs, feature‑embedding networks, and finally a Wide‑Deep multi‑task model—while tackling data sparsity, diverse scenarios, and deploying the system via TensorFlow‑Serving and the in‑house MLX platform.

Deep LearningMeituanNLP

0 likes · 33 min read

Deep Learning and Ranking Model Evolution for Hotel Search at Meituan

Meituan Technology Team

Jun 21, 2018 · Artificial Intelligence

Deep Learning for Text Matching and Ranking at Meituan

Meituan leverages deep‑learning models such as Word2Vec, DSSM, and LSTM‑based encoders within its ClickNet framework to compute text similarity and rank results, integrating rich business features like user location and merchant rating, thereby surpassing traditional TF‑IDF, BM25, and XGBoost approaches and boosting click‑through rates and revenue.

AIDeep LearningRanking Models

0 likes · 27 min read

Deep Learning for Text Matching and Ranking at Meituan

ITFLY8 Architecture Home

Jun 7, 2018 · Artificial Intelligence

How Modern Recommendation Systems Work: Architecture, Algorithms, and Best Practices

This article explains the goals, architectures, data pipelines, recall strategies, and ranking models of contemporary recommendation systems, covering both online and offline components, collaborative filtering, content-based methods, feature engineering, and practical interview insights for engineers.

Ranking Modelscollaborative filteringmachine learning

0 likes · 18 min read

How Modern Recommendation Systems Work: Architecture, Algorithms, and Best Practices