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DataFunSummit
DataFunSummit
May 22, 2026 · Artificial Intelligence

Why Memory Is the Bottleneck for AI Agents and How MemOS Achieves 200% Cloud Call Growth

The article analyses how memory has become the critical limitation for AI agents, details the MemOS framework’s five‑layer architecture that fuses model‑driven and application‑driven approaches, presents cloud service usage surging over 200%, and explains how these advances address scalability, privacy, and performance challenges in enterprise deployments.

AI memoryAgent ArchitectureCloud AI services
0 likes · 18 min read
Why Memory Is the Bottleneck for AI Agents and How MemOS Achieves 200% Cloud Call Growth
AntData
AntData
May 14, 2026 · Artificial Intelligence

How RAG‑Powered DB‑GPT Enables Intelligent Marine‑Environment Queries with Text2SQL

The article presents a private‑deployed DB‑GPT solution that combines Retrieval‑Augmented Generation (RAG) and Text2SQL to address low utilization of unstructured marine‑environment knowledge, cross‑source data querying difficulties, and security concerns, detailing technical selection, implementation steps, and performance gains that reduce query time from 30 minutes to 1‑3 minutes.

AIDB-GPTKnowledge retrieval
0 likes · 13 min read
How RAG‑Powered DB‑GPT Enables Intelligent Marine‑Environment Queries with Text2SQL
AI Illustrated Series
AI Illustrated Series
Apr 27, 2026 · Artificial Intelligence

Comprehensive RAG Interview Q&A: 22 In-Depth Questions and Answers

This extensive interview guide covers 22 core RAG questions, detailing the definition, workflow, embedding selection, vector database choices, retrieval optimization, multi‑turn handling, context compression, evaluation metrics, knowledge‑graph integration, operational challenges, Agentic and hybrid RAG, document update strategies, similarity algorithms, and hallucination mitigation, providing concrete examples and practical advice for AI interview preparation.

AI InterviewEmbeddingKnowledge retrieval
0 likes · 29 min read
Comprehensive RAG Interview Q&A: 22 In-Depth Questions and Answers
HyperAI Super Neural
HyperAI Super Neural
Apr 14, 2026 · Artificial Intelligence

DeepTutor Online Tutorial: HKU’s Open‑Source Multi‑Agent Interactive Learning Assistant

DeepTutor, an open‑source personal learning assistant from HKU’s Data Science Lab, combines multi‑agent collaboration, retrieval‑augmented generation, and web search to deliver end‑to‑end interactive learning—covering knowledge Q&A, visual explanations, exercise generation, and research support—while a step‑by‑step HyperAI tutorial shows how to deploy it with ready‑made compute resources.

AI tutoringDeepTutorHyperAI
0 likes · 6 min read
DeepTutor Online Tutorial: HKU’s Open‑Source Multi‑Agent Interactive Learning Assistant
AI Step-by-Step
AI Step-by-Step
Mar 29, 2026 · Artificial Intelligence

How RAG Quickly Gives Your Agent Real Business Knowledge

The article explains why agents often lack business understanding, describes Retrieval‑Augmented Generation (RAG) as the fastest way to provide correct, up‑to‑date business context, outlines eight practical RAG patterns, and offers a step‑by‑step checklist for building enterprise‑ready agents.

AgentGraphRAGKnowledge retrieval
0 likes · 10 min read
How RAG Quickly Gives Your Agent Real Business Knowledge
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 23, 2026 · Artificial Intelligence

From Scenario Abstraction to an AI Assistant Production Line: Scalable Architecture and Prompt Plug‑In Design

The article analyzes the inefficiencies of building isolated AI assistants for each business need, abstracts four high‑frequency scenarios, proposes a reusable technical solution stack—including IntentResult modeling, FSWW tool‑recall, ReAct reasoning, multimodal RAG, and a prompt plug‑in framework—and demonstrates how a one‑click platform can turn these designs into production‑ready AI assistants.

AI assistantsKnowledge retrievalLLM architecture
0 likes · 21 min read
From Scenario Abstraction to an AI Assistant Production Line: Scalable Architecture and Prompt Plug‑In Design
PMTalk Product Manager Community
PMTalk Product Manager Community
Feb 13, 2026 · Artificial Intelligence

From Zero to One: Building a Deployable RAG System for Intelligent Customer Service

This article walks product managers through the end‑to‑end design of a Retrieval‑Augmented Generation (RAG) intelligent‑customer‑service system, covering business value, knowledge‑base preparation, hybrid retrieval, prompt‑driven generation, deployment choices, monitoring metrics, and common methodological pitfalls.

AI ArchitectureIntelligent Customer ServiceKnowledge retrieval
0 likes · 11 min read
From Zero to One: Building a Deployable RAG System for Intelligent Customer Service
Architecture and Beyond
Architecture and Beyond
Dec 21, 2025 · Artificial Intelligence

Designing RAG for Industry‑Specific AI Agents: From Data to Safe Execution

This article explains how to build Retrieval‑Augmented Generation (RAG) for industry‑specific AI agents, covering required capabilities, metrics, data sources, indexing, hybrid retrieval, decision‑point integration, layered output, permission controls, rollout strategies, and common pitfalls to ensure reliable and secure automation.

Agent DesignKnowledge retrievalRAG
0 likes · 17 min read
Designing RAG for Industry‑Specific AI Agents: From Data to Safe Execution
Alibaba Cloud Native
Alibaba Cloud Native
Aug 26, 2025 · Artificial Intelligence

Boost Dify’s RAG Performance with Higress AI Gateway: Two Integration Strategies

This guide explains how to overcome Dify's built‑in RAG limitations by using Higress AI Gateway to connect external RAG services, detailing two integration patterns—RAG Retrieval Agent and Automatic Retrieval Injection—along with step‑by‑step configuration, validation, and the resulting benefits for enterprise AI applications.

DifyKnowledge retrievalRAG
0 likes · 13 min read
Boost Dify’s RAG Performance with Higress AI Gateway: Two Integration Strategies
Tencent Cloud Developer
Tencent Cloud Developer
Jul 23, 2025 · Artificial Intelligence

Why Retrieval‑Augmented Generation Is Evolving Into Agentic AI Search

This article explains how the inherent knowledge limits of large language models drive the rise of Retrieval‑Augmented Generation (RAG), outlines its three evolutionary stages, introduces Agentic RAG and DeepSearch, and discusses the knowledge and ability boundaries that shape future AI search systems.

AI SearchDeepSearchKnowledge retrieval
0 likes · 19 min read
Why Retrieval‑Augmented Generation Is Evolving Into Agentic AI Search
AntTech
AntTech
Jul 9, 2025 · Artificial Intelligence

How KAG-Thinker Boosts Structured Reasoning in Large Language Models

The KAG-Thinker model, a collaborative effort by Ant Group, Zhejiang University, and Tongji University, introduces a hierarchical "breadth splitting + depth solving" framework that enhances logical stability, knowledge utilization, and retrieval robustness for complex multi‑hop reasoning tasks across general and specialized domains.

AIKAG-ThinkerKnowledge retrieval
0 likes · 10 min read
How KAG-Thinker Boosts Structured Reasoning in Large Language Models
IT Services Circle
IT Services Circle
Jun 6, 2025 · Artificial Intelligence

Master Retrieval‑Augmented Generation (RAG): From Basics to Advanced Practices

This article introduces Retrieval‑Augmented Generation (RAG), explains its core components—knowledge embedding, retriever, and generator—covers practical system construction, optimization techniques, evaluation metrics, and advanced paradigms such as GraphRAG and Multi‑Modal RAG, while highlighting a comprehensive guidebook for hands‑on implementation.

AIKnowledge retrievalRAG
0 likes · 12 min read
Master Retrieval‑Augmented Generation (RAG): From Basics to Advanced Practices
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 5, 2025 · Artificial Intelligence

How Deep (Re)Search Transforms Code Search and AI-Powered Knowledge Retrieval

This article systematically explains the concepts of Deep Search and Deep Research, contrasts them with traditional Retrieval‑Augmented Generation, reviews leading commercial and open‑source solutions, details their architecture for code retrieval, and outlines future plans for specialized code‑search agents.

AI researchCode searchKnowledge retrieval
0 likes · 13 min read
How Deep (Re)Search Transforms Code Search and AI-Powered Knowledge Retrieval
Tencent Technical Engineering
Tencent Technical Engineering
May 19, 2025 · Artificial Intelligence

RAG, Agents, and Multimodal Large Models: Evolution, Challenges, and Future Trends

This article examines the evolution of large model technologies—including Retrieval‑Augmented Generation, AI agents, and multimodal models—detailing their technical foundations, practical challenges, industry applications, and future development trends, offering a comprehensive perspective for AI practitioners and researchers.

AI agentKnowledge retrievalMultimodal
0 likes · 14 min read
RAG, Agents, and Multimodal Large Models: Evolution, Challenges, and Future Trends
DataFunTalk
DataFunTalk
Apr 24, 2025 · Artificial Intelligence

Is Retrieval‑Augmented Generation (RAG) Dead Yet?

This article explains the original purpose of Retrieval‑Augmented Generation, why it remains essential despite advances in large‑context LLMs, and how combining RAG with fine‑tuning, longer context windows, and model‑context protocols yields more scalable, accurate, and privacy‑preserving AI systems.

AIKnowledge retrievalRAG
0 likes · 9 min read
Is Retrieval‑Augmented Generation (RAG) Dead Yet?
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Apr 8, 2025 · Artificial Intelligence

What Is Retrieval‑Augmented Generation (RAG) and How Does It Boost AI Accuracy?

This article explains Retrieval‑Augmented Generation (RAG), its three‑step workflow of retrieval, augmentation, and generation, its key advantages such as improved accuracy and explainability, and compares RAG with traditional pre‑trained models, fine‑tuned models, hybrid models, knowledge‑distillation methods, and RLHF, while also covering vector, full‑text, and hybrid retrieval modes and the role of rerank models.

AIKnowledge retrievalRAG
0 likes · 18 min read
What Is Retrieval‑Augmented Generation (RAG) and How Does It Boost AI Accuracy?
DevOps
DevOps
Apr 2, 2025 · Artificial Intelligence

Understanding Retrieval-Augmented Generation (RAG): Concepts, Evolution, and Types

This article explains Retrieval‑Augmented Generation (RAG), its role in mitigating large language model knowledge cutoff and hallucination, outlines the evolution from naive to advanced, modular, graph, and agentic RAG, and discusses future directions such as intelligent and multi‑modal RAG systems.

Artificial IntelligenceKnowledge retrievalLLM
0 likes · 10 min read
Understanding Retrieval-Augmented Generation (RAG): Concepts, Evolution, and Types
Tencent Cloud Developer
Tencent Cloud Developer
Apr 2, 2025 · Artificial Intelligence

Understanding Retrieval‑Augmented Generation (RAG): Concepts, Types, and Development

Retrieval‑Augmented Generation (RAG) enhances large language models by fetching up‑to‑date external knowledge before generation, mitigating knowledge‑cutoff limits and hallucinations through a retrieval step (using text, vector, or graph methods) and a generation step, evolving from naive single‑method approaches to advanced, modular, graph‑based, and agentic systems that enable adaptive, multi‑hop reasoning and future intelligent, multimodal pipelines.

AIHallucination MitigationKnowledge retrieval
0 likes · 9 min read
Understanding Retrieval‑Augmented Generation (RAG): Concepts, Types, and Development
Architect
Architect
Mar 30, 2025 · Artificial Intelligence

What Is Retrieval-Augmented Generation? A Deep Dive into RAG Techniques

This article provides a comprehensive survey of Retrieval‑Augmented Generation (RAG), covering its basic principles, key components, seven technical variants, challenges, evaluation methods, and future research directions across multimodal, graph‑based, and agentic extensions.

AI SurveyKnowledge retrievalRAG
0 likes · 9 min read
What Is Retrieval-Augmented Generation? A Deep Dive into RAG Techniques
Ops Development & AI Practice
Ops Development & AI Practice
Mar 19, 2025 · Artificial Intelligence

Can Cache‑Augmented Generation Outperform RAG? A Deep Dive into LLM Efficiency

Cache‑augmented generation (CAG) preloads documents into LLM context using KV caches to eliminate retrieval latency, offering faster inference for static knowledge bases, while RAG remains more flexible for dynamic or large corpora; this article compares their definitions, performance, implementation steps, and future prospects.

CAGCache AugmentationInference Optimization
0 likes · 11 min read
Can Cache‑Augmented Generation Outperform RAG? A Deep Dive into LLM Efficiency
Cognitive Technology Team
Cognitive Technology Team
Feb 28, 2025 · Artificial Intelligence

Comparative Study of Traditional RAG, GraphRAG, and DeepSearcher for Knowledge Retrieval and Generation

This article examines why Retrieval‑Augmented Generation (RAG) is needed, compares traditional RAG, GraphRAG, and the DeepSearcher framework across architecture, data organization, retrieval mechanisms, result generation, efficiency and accuracy, and provides step‑by‑step implementation guides and experimental results using vector and graph databases.

Artificial IntelligenceDeepSearcherGraph Database
0 likes · 20 min read
Comparative Study of Traditional RAG, GraphRAG, and DeepSearcher for Knowledge Retrieval and Generation
NewBeeNLP
NewBeeNLP
Dec 16, 2024 · Artificial Intelligence

How Tencent Boosts LLM Power with RAG, GraphRAG, and Agent Technologies

This article examines Tencent's large language model deployments across content generation, intelligent customer service, and role‑playing scenarios, detailing the principles and practical implementations of Retrieval‑Augmented Generation (RAG), GraphRAG, and Agent techniques, and discusses challenges, optimization strategies, and real‑world use cases.

AIAgentGraphRAG
0 likes · 18 min read
How Tencent Boosts LLM Power with RAG, GraphRAG, and Agent Technologies
DaTaobao Tech
DaTaobao Tech
Oct 23, 2024 · Artificial Intelligence

Retrieval-Augmented Generation (RAG): Principles, Applications, Limitations and Challenges

Retrieval-Augmented Generation (RAG) combines a retriever that fetches relevant external documents and a generator that uses them, improving LLM accuracy, relevance, privacy, and up-to-date information, but faces challenges such as retrieval latency, computational cost, chunking strategies, embedding selection, and system integration complexity.

AIKnowledge retrievalLLM
0 likes · 13 min read
Retrieval-Augmented Generation (RAG): Principles, Applications, Limitations and Challenges
Java Tech Enthusiast
Java Tech Enthusiast
Jul 23, 2024 · Industry Insights

Can Baidu’s Orange Paper Outperform Kimi? A Deep Dive into AI Writing Tools

This article compares Baidu’s new AI writing platform Orange Paper with Kimi, evaluating their long‑text understanding, multimodal editing, document upload limits, outline generation, and overall usability for research and academic writing, highlighting Orange Paper’s advantages in knowledge retrieval, large‑scale content creation, and deep editing capabilities.

AI writingKnowledge retrievalLong Text Generation
0 likes · 11 min read
Can Baidu’s Orange Paper Outperform Kimi? A Deep Dive into AI Writing Tools
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 11, 2024 · Artificial Intelligence

Mastering Retrieval‑Augmented Generation: Challenges, Paradigms, and Engineering Best Practices

This article explores Retrieval‑Augmented Generation (RAG) by outlining its background, inherent challenges such as knowledge limits and hallucinations, describing the Naïve, Advanced, and Modular RAG paradigms, and presenting practical engineering strategies for pre‑retrieval, retrieval, and post‑retrieval optimization.

Artificial IntelligenceKnowledge retrievalMachine Learning
0 likes · 25 min read
Mastering Retrieval‑Augmented Generation: Challenges, Paradigms, and Engineering Best Practices
JD Retail Technology
JD Retail Technology
May 22, 2024 · Artificial Intelligence

AI Multi‑Agent System for E‑commerce: Design, Implementation, and Operational Insights

This article presents a comprehensive overview of JD Retail's AI‑driven multi‑agent architecture for e‑commerce assistance, detailing how real‑world merchant decision processes are modeled with ReAct‑based LLM agents, the hierarchical workflow, training pipelines, monitoring mechanisms, and future directions for scalable intelligent commerce support.

AIAgent ArchitectureKnowledge retrieval
0 likes · 20 min read
AI Multi‑Agent System for E‑commerce: Design, Implementation, and Operational Insights
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Apr 8, 2024 · Artificial Intelligence

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

The article introduces PreFLMR, an open‑source, general‑purpose pre‑trained multimodal retriever that leverages fine‑grained late‑interaction to boost retrieval‑augmented generation for knowledge‑intensive visual tasks, describes its M2KR benchmark, training stages, and strong experimental results across multiple tasks.

AIFLMRKnowledge retrieval
0 likes · 11 min read
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
Baobao Algorithm Notes
Baobao Algorithm Notes
Nov 9, 2023 · Artificial Intelligence

Building High‑Performance Vertical Domain LLMs: From Continued Pre‑Training to Retrieval‑Augmented Generation

This article systematically explains how to create vertical domain large language models by continuing pre‑training on domain data, constructing fine‑tuning datasets with self‑instruct, reducing hallucinations, and integrating knowledge retrieval, while also reviewing related papers, products, and system architectures.

AI researchKnowledge retrievalself-instruct
0 likes · 21 min read
Building High‑Performance Vertical Domain LLMs: From Continued Pre‑Training to Retrieval‑Augmented Generation
Tencent Tech
Tencent Tech
Sep 20, 2023 · Artificial Intelligence

Why Do Large Language Models Hallucinate and How to Reduce It?

The article explains why large language models generate hallucinations—due to data errors, training conflicts, and inference uncertainty—and outlines data‑cleaning, model‑level feedback, knowledge augmentation, constraint techniques, and post‑processing methods such as the “Truth‑seeking” algorithm to mitigate the issue.

AI safetyKnowledge retrievaldata quality
0 likes · 8 min read
Why Do Large Language Models Hallucinate and How to Reduce It?
Tencent Cloud Developer
Tencent Cloud Developer
Jun 28, 2023 · Artificial Intelligence

Prompt Engineering: Fundamentals, Techniques, and Advanced Strategies

Prompt engineering teaches how to craft effective instructions, context, input data, and output formats for large language models, using clear commands, iterative refinement, and advanced methods such as zero‑shot, few‑shot, chain‑of‑thought, Tree of Thoughts, retrieval‑augmented and progressive‑hint prompting to achieve precise, reliable results across diverse tasks.

AIKnowledge retrievalMultimodal
0 likes · 17 min read
Prompt Engineering: Fundamentals, Techniques, and Advanced Strategies
DataFunSummit
DataFunSummit
Feb 24, 2023 · Artificial Intelligence

Baidu PLATO Open‑Domain Dialogue Model: Technology, Challenges, and Applications

The article presents Baidu's PLATO open‑domain dialogue system, detailing its evolution from expert‑rule to retrieval‑based and large‑scale generative models, describing its hidden‑variable architecture, major research challenges such as persona stability, long‑term memory, knowledge accuracy, and showcasing real‑world applications and Q&A from a DataFunSummit2022 livestream.

AIKnowledge retrievalLarge Language Model
0 likes · 25 min read
Baidu PLATO Open‑Domain Dialogue Model: Technology, Challenges, and Applications