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Retrieval-Augmented Generation

185 articles · Page 2 of 2
Tech Freedom Circle
Tech Freedom Circle
Sep 25, 2025 · Artificial Intelligence

Inside RAGFlow: How Its Microservice Architecture Powers an Enterprise‑Grade Retrieval‑Augmented Generation Platform

This article provides a detailed technical walkthrough of RAGFlow's architecture, covering its microservice design, directory layout, layered structure, cloud‑native deployment, core modules such as DeepDoc, RAG engine, Agent system, and web UI, as well as multi‑tenant isolation, streaming responses, asynchronous task handling, concurrency controls, scalability strategies, and a complete request‑lifecycle example for document upload.

AI architectureDeepDocDocker Compose
0 likes · 26 min read
Inside RAGFlow: How Its Microservice Architecture Powers an Enterprise‑Grade Retrieval‑Augmented Generation Platform
Huolala Tech
Huolala Tech
Sep 24, 2025 · Artificial Intelligence

How CID-GraphRAG Boosts Multi‑Turn AI Customer Service with Dual‑Layer Retrieval

The article introduces CID-GraphRAG, a novel framework that combines intent‑driven graphs with semantic similarity search to improve multi‑turn intelligent customer service, detailing its architecture, dual‑layer retrieval mechanism, evaluation against baseline models, and future research directions.

AICustomer ServiceDialogue Systems
0 likes · 14 min read
How CID-GraphRAG Boosts Multi‑Turn AI Customer Service with Dual‑Layer Retrieval
Data Thinking Notes
Data Thinking Notes
Sep 21, 2025 · Artificial Intelligence

From RAG to DeepSearch & DeepResearch: How AI Is Mastering Knowledge Retrieval

Amid the rapid rise of generative AI, this article examines the limitations of large language models and explains how Retrieval‑Augmented Generation (RAG), followed by the advanced paradigms DeepSearch and DeepResearch, progressively enhance knowledge handling through dynamic retrieval, multi‑agent reasoning, and autonomous research capabilities.

AI Knowledge ManagementDeepResearchDeepSearch
0 likes · 16 min read
From RAG to DeepSearch & DeepResearch: How AI Is Mastering Knowledge Retrieval
DataFunTalk
DataFunTalk
Sep 19, 2025 · Artificial Intelligence

How Tencent’s Large Language Models Transform Business with RAG, GraphRAG, and Agents

This article examines Tencent's large language model deployments across diverse business scenarios, detailing how Retrieval‑Augmented Generation, GraphRAG, and autonomous agents boost model intelligence, improve user experience, and enable advanced content generation, understanding, and multi‑step reasoning.

Artificial IntelligenceAutonomous AgentsGraphRAG
0 likes · 4 min read
How Tencent’s Large Language Models Transform Business with RAG, GraphRAG, and Agents
Architecture & Thinking
Architecture & Thinking
Sep 12, 2025 · Artificial Intelligence

How Knowledge Graphs Turn Large Language Models into Trustworthy Experts

Integrating structured knowledge graphs with generative AI provides traceable, explainable, and high‑precision reasoning across domains such as medicine, finance, and law, through techniques like Retrieval‑Augmented Generation, graph neural networks, and adaptive planning, dramatically reducing hallucinations and boosting expert‑level performance.

AI hallucinationGraph Neural NetworkKnowledge Graph
0 likes · 12 min read
How Knowledge Graphs Turn Large Language Models into Trustworthy Experts
Architects Research Society
Architects Research Society
Sep 10, 2025 · Artificial Intelligence

From Vectors to Graphs to Hybrids: The Evolution of AI Knowledge Representation

This article explores the three stages of AI knowledge representation—vector embeddings, graph‑based structures, and the emerging hybrid approach that combines vectors, graphs, and large language models—to illustrate how modern Retrieval‑Augmented Generation systems achieve both semantic similarity and precise relational reasoning.

AIGraph DatabasesRetrieval-Augmented Generation
0 likes · 3 min read
From Vectors to Graphs to Hybrids: The Evolution of AI Knowledge Representation
Instant Consumer Technology Team
Instant Consumer Technology Team
Sep 5, 2025 · Artificial Intelligence

Why Context Engineering Is the Next Frontier for Large Language Models

This article surveys over 1,400 papers to define context engineering as a systematic discipline that structures retrieval, memory, tools, and multi‑agent coordination for LLMs, highlighting the critical asymmetry between understanding long contexts and generating equally complex outputs.

Context EngineeringLLM evaluationLarge Language Models
0 likes · 8 min read
Why Context Engineering Is the Next Frontier for Large Language Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 1, 2025 · Artificial Intelligence

Mastering RAG: From Chunking to Hybrid Search for Better AI Retrieval

This article delves into the implementation details and optimization strategies of Retrieval‑Augmented Generation (RAG), covering document chunking, index enhancement, embedding, hybrid search, and re‑ranking, and provides practical code examples to help developers move from quick deployment to deep performance tuning.

AIChunkingEmbedding
0 likes · 19 min read
Mastering RAG: From Chunking to Hybrid Search for Better AI Retrieval
Tech Freedom Circle
Tech Freedom Circle
Aug 26, 2025 · Artificial Intelligence

How to Optimize RAG for Alibaba Interviews? 7 Golden Rules Explained

This article provides a step‑by‑step technical guide to optimizing Retrieval‑Augmented Generation (RAG) for interview scenarios, covering query rewriting, HyDE, fallback strategies, routing and prompt routing, multi‑representation indexing, hybrid retrieval, re‑ranking, self‑RAG, generation control, performance benchmarking, and a practical checklist with concrete code examples and metrics.

AI InterviewHybrid RetrievalLangChain
0 likes · 30 min read
How to Optimize RAG for Alibaba Interviews? 7 Golden Rules Explained
DaTaobao Tech
DaTaobao Tech
Aug 25, 2025 · Artificial Intelligence

Mastering RAG: From Quick Start to Deep Optimization Strategies

This article dives into the practical implementation of Retrieval‑Augmented Generation (RAG), covering document chunking, semantic and reverse HyDE indexing, embedding, hybrid search, and re‑ranking techniques, and provides concrete code examples and optimization tips for building high‑performance AI applications.

Artificial IntelligenceChunkingEmbedding
0 likes · 18 min read
Mastering RAG: From Quick Start to Deep Optimization Strategies
Volcano Engine Developer Services
Volcano Engine Developer Services
Aug 21, 2025 · Artificial Intelligence

Why Prompt Engineering Isn’t Enough: The Rise of Context Engineering and RAG

Since last year, the debate over “Prompt Engineering” has split between practitioners who favor “Context Engineering” for building scalable agent systems and scholars who treat Prompt Engineering as a broad umbrella term, highlighting the need to dynamically construct and manage context for reliable, extensible AI applications.

AI agentsLLMPrompt Engineering
0 likes · 33 min read
Why Prompt Engineering Isn’t Enough: The Rise of Context Engineering and RAG
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Jul 30, 2025 · Artificial Intelligence

How MCP‑RAG Overcomes Prompt Inflation for Massive LLM Service Calls

This article analyzes the prompt‑inflation bottleneck that arises when large language models (LLMs) must handle thousands of Model Context Protocol (MCP) services, and introduces the MCP‑RAG architecture—a retrieval‑augmented generation solution that builds a metadata knowledge base and intelligent retrieval layer to enable precise, efficient MCP service discovery at scale.

AILLMMCP
0 likes · 21 min read
How MCP‑RAG Overcomes Prompt Inflation for Massive LLM Service Calls
JD Tech
JD Tech
Jul 29, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

This article describes a QCon talk that combines causal inference with large language models to build a retrieval‑augmented generation pricing system for e‑commerce, detailing the three‑step algorithm, LLM‑driven modeling challenges, process‑reward tree search, reinforcement‑learning fine‑tuning, and experimental gains in accuracy and speed.

Large Language ModelsRetrieval-Augmented Generationcausal inference
0 likes · 17 min read
How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Jul 24, 2025 · Artificial Intelligence

Exploring Recent Large‑Model Agent Papers: Insights and Analyses

This article reviews a series of recent research papers on large‑model agents, covering topics such as reinforcement‑learning‑driven ML agents, premise‑critique ability of LLMs, long‑term tool‑augmented LLM evaluation, agentic RAG, set‑based retrieval for multi‑hop QA, mobile VLM agents, and broader surveys of LLM applications, summarizing each work’s problem statement, prior approaches, novel contributions, experimental results, limitations, and future directions.

LLM evaluationLarge Language ModelsRetrieval-Augmented Generation
0 likes · 46 min read
Exploring Recent Large‑Model Agent Papers: Insights and Analyses
DataFunTalk
DataFunTalk
Jul 21, 2025 · Artificial Intelligence

From Prompt Engineering to Context Engineering: Transforming LLM Interactions

This article traces the evolution from prompt engineering to context engineering, detailing technical milestones, core concepts, practical strategies, and future trends that together reshape large language model applications and enable sophisticated AI agents across diverse domains.

Large Language ModelsMemory ManagementPrompt Engineering
0 likes · 35 min read
From Prompt Engineering to Context Engineering: Transforming LLM Interactions
Instant Consumer Technology Team
Instant Consumer Technology Team
Jul 14, 2025 · Artificial Intelligence

9 Essential Technologies for Building Scalable AI Agents

An in‑depth guide outlines the nine core technologies—ranging from autonomous agent fundamentals and multi‑agent collaboration to workflow orchestration, retrieval‑augmented generation, fine‑tuning, function calling, model context protocols, agent‑to‑agent communication, and AI‑driven UI—required to design, deploy, and scale enterprise‑grade AI agents.

AI agentsAgent ArchitectureFunction Calling
0 likes · 9 min read
9 Essential Technologies for Building Scalable AI Agents
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jul 12, 2025 · Artificial Intelligence

Why GraphRAG Is the Future of Retrieval‑Augmented Generation

This article explains how GraphRAG combines knowledge graphs with retrieval‑augmented generation to overcome the limitations of vector‑only RAG, delivering higher accuracy, better explainability, easier development, and stronger governance for generative AI applications across various domains.

AIGraphRAGKnowledge Graph
0 likes · 23 min read
Why GraphRAG Is the Future of Retrieval‑Augmented Generation
Hailey Says
Hailey Says
Jul 6, 2025 · Artificial Intelligence

How Retrieval‑Augmented Generation Lets LLMs Actively Gather Quotes Before Responding

The article explains Retrieval‑Augmented Generation (RAG), detailing its three‑step workflow—retrieval, augmentation, generation—along with architecture components, data indexing, vector‑database choices, prompt construction, and challenges such as noise, token limits, and model accuracy, illustrating how RAG enables LLMs to fetch relevant quotes before answering.

Knowledge RetrievalLLMPrompt Engineering
0 likes · 9 min read
How Retrieval‑Augmented Generation Lets LLMs Actively Gather Quotes Before Responding
AI Algorithm Path
AI Algorithm Path
Jul 3, 2025 · Artificial Intelligence

Exploring Advanced, Graph, and Agentic RAG: The Evolution of Retrieval‑Augmented Generation

This article examines how Retrieval‑Augmented Generation (RAG) has progressed from simple keyword‑based retrieval to advanced semantic methods, modular architectures, graph‑enhanced reasoning, and autonomous agentic systems, highlighting each approach's workflow, benefits, limitations, and the shift toward dynamic AI decision‑making.

AIAgentic RAGKnowledge Graph
0 likes · 7 min read
Exploring Advanced, Graph, and Agentic RAG: The Evolution of Retrieval‑Augmented Generation
AIWalker
AIWalker
Jun 30, 2025 · Artificial Intelligence

Chinese Team Builds First AI That Understands Film, Using 440K Shot Library for Director‑Level Camera Moves

FilMaster is a pioneering AI system that learns cinematic principles from a 440,000‑shot movie database, combines multimodal LLMs, RAG, and audience‑centric rhythm control to generate editable, high‑quality films, and outperforms prior methods by over 50% on the new FilmEval benchmark.

AI film generationFilmEval benchmarkMultimodal Models
0 likes · 18 min read
Chinese Team Builds First AI That Understands Film, Using 440K Shot Library for Director‑Level Camera Moves
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Jun 6, 2025 · Artificial Intelligence

Tackling the Top Challenges of Retrieval‑Augmented Generation (RAG)

The article enumerates common pitfalls of Retrieval‑Augmented Generation—such as missing content, low‑rank document misses, context limits, format errors, incomplete answers, scalability bottlenecks, complex PDF extraction, data‑quality issues, domain adaptation gaps, hallucinations, and feedback‑loop deficiencies—and offers concrete mitigation strategies ranging from data cleaning and prompt design to hybrid search, hierarchical retrieval, document compression, and automated evaluation.

Hybrid SearchLLMPrompt Engineering
0 likes · 9 min read
Tackling the Top Challenges of Retrieval‑Augmented Generation (RAG)
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
Instant Consumer Technology Team
Instant Consumer Technology Team
Jun 5, 2025 · Artificial Intelligence

How DeepSeek AI Transforms Government Search with Smarter, Faster Answers

This article explains how DeepSeek's large‑model‑driven search system overcomes traditional keyword‑matching limits, improves long‑tail query coverage, and delivers personalized, accurate government service results through intent parsing, knowledge‑graph retrieval, and generative optimization.

Artificial IntelligenceRetrieval-Augmented GenerationSearch Optimization
0 likes · 9 min read
How DeepSeek AI Transforms Government Search with Smarter, Faster Answers
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 searchDeep Search
0 likes · 13 min read
How Deep (Re)Search Transforms Code Search and AI-Powered Knowledge Retrieval
Ubiquitous Tech
Ubiquitous Tech
May 25, 2025 · Artificial Intelligence

Understanding RAG: The Core Capability Behind AI Customer Service

This article explains why Retrieval‑Augmented Generation (RAG) is essential for AI‑driven customer service, outlines the limitations of large language models, details the three‑stage RAG workflow (indexing, retrieval, generation), and shows how to implement it with FastGPT, vector databases, and LangChain.

AI Customer ServiceFastGPTKnowledge Base
0 likes · 16 min read
Understanding RAG: The Core Capability Behind AI Customer Service
Architect
Architect
May 5, 2025 · Artificial Intelligence

How Agentic RAG‑R1 Turns Retrieval‑Augmented Generation into an Autonomous AI Agent

Agentic RAG‑R1, an open‑source project from Peking University, combines Retrieval‑Augmented Generation with an agentic AI loop, introduces the GRPO reinforcement‑learning optimizer, supports LoRA‑based fine‑tuning, quantization and multimodal tool calls, and demonstrates significant accuracy gains on the MedQA benchmark across both Chinese and English test sets.

LLM Tool UseLarge Language ModelsRetrieval-Augmented Generation
0 likes · 8 min read
How Agentic RAG‑R1 Turns Retrieval‑Augmented Generation into an Autonomous AI Agent
DataFunSummit
DataFunSummit
Apr 21, 2025 · Artificial Intelligence

Deep Integration of Knowledge Graphs and Large Language Models: Methods, Applications, and Future Directions

This article explores how knowledge graphs can be tightly integrated with large language models through prompt engineering, fine‑tuning, retrieval‑augmented generation, reasoning collaboration, and knowledge agents, outlining technical pathways, practical implementations, and future research directions across AI domains.

AIKnowledge GraphRetrieval-Augmented Generation
0 likes · 23 min read
Deep Integration of Knowledge Graphs and Large Language Models: Methods, Applications, and Future Directions
Sohu Tech Products
Sohu Tech Products
Apr 9, 2025 · Artificial Intelligence

Boost LLM Retrieval Accuracy with MCP: A Step‑by‑Step Guide

This tutorial explains how to overcome the limitations of Retrieval‑Augmented Generation by using the Model Context Protocol (MCP) together with a MongoDB database, providing detailed setup steps, configuration examples, and performance comparisons that demonstrate significantly higher query precision for large language models.

AI agentsFunction CallMCP
0 likes · 24 min read
Boost LLM Retrieval Accuracy with MCP: A Step‑by‑Step Guide
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?
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.

AIKnowledge RetrievalRAG
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 RetrievalLarge Language Models
0 likes · 9 min read
What Is Retrieval-Augmented Generation? A Deep Dive into RAG Techniques
Alibaba Cloud Native
Alibaba Cloud Native
Mar 19, 2025 · Artificial Intelligence

Mastering Retrieval‑Augmented Generation with Spring AI: A Complete Guide

This article explains the Retrieval‑Augmented Generation (RAG) paradigm, walks through its four core steps, and provides a detailed Spring AI implementation—including configuration, vector storage, REST controller, multi‑query expansion, query rewriting, document joining, and error handling—plus best‑practice recommendations for production deployments.

AIRAGRetrieval-Augmented Generation
0 likes · 23 min read
Mastering Retrieval‑Augmented Generation with Spring AI: A Complete Guide
DaTaobao Tech
DaTaobao Tech
Mar 19, 2025 · Artificial Intelligence

Retrieval Augmented Generation (RAG): Principles, Challenges, and Implementation Techniques

Retrieval‑augmented generation (RAG) enhances large language models by integrating a preprocessing pipeline—cleaning, chunking, embedding, and vector storage—with a query‑driven retrieval and prompt‑injection workflow, leveraging vector databases, multi‑stage recall, advanced prompting, and comprehensive evaluation metrics to mitigate knowledge cut‑off, hallucinations, and security issues.

LLMRAGRetrieval-Augmented Generation
0 likes · 27 min read
Retrieval Augmented Generation (RAG): Principles, Challenges, and Implementation Techniques
AIWalker
AIWalker
Mar 18, 2025 · Artificial Intelligence

How ImageRAG Boosts Text‑to‑Image Generation with Retrieval‑Augmented Generation

ImageRAG introduces a retrieval‑augmented generation framework that dynamically fetches relevant images to guide diffusion models, dramatically improving the synthesis of rare and fine‑grained concepts across multiple text‑to‑image systems, as demonstrated by extensive quantitative and user studies.

AI generationImageRAGRetrieval-Augmented Generation
0 likes · 17 min read
How ImageRAG Boosts Text‑to‑Image Generation with Retrieval‑Augmented Generation
AI Algorithm Path
AI Algorithm Path
Mar 11, 2025 · Artificial Intelligence

AI Agents Overview: Foundations, Core Components, and When to Use Them

This article provides a comprehensive overview of AI Agents, tracing their evolution from traditional chatbots to LLM‑driven agents, explaining core components such as perception, reasoning, action, knowledge bases, learning and communication interfaces, and discussing practical use cases, interaction cycles, and future prospects.

AI agentsAutonomous SystemsLarge Language Models
0 likes · 15 min read
AI Agents Overview: Foundations, Core Components, and When to Use Them
Ma Wei Says
Ma Wei Says
Feb 25, 2025 · Artificial Intelligence

What Is GraphRAG? A Deep Dive into Next‑Gen Retrieval‑Augmented Generation and Open‑Source Implementations

GraphRAG, the next generation of Retrieval‑Augmented Generation, combines large language models, knowledge graphs, and graph databases to overcome traditional RAG’s knowledge gaps, hallucinations, and context limitations, and the article reviews its architecture, core modules, a recent 2025 paper, and six notable open‑source implementations.

Artificial IntelligenceGraphRAGRetrieval-Augmented Generation
0 likes · 9 min read
What Is GraphRAG? A Deep Dive into Next‑Gen Retrieval‑Augmented Generation and Open‑Source Implementations
Tencent Technical Engineering
Tencent Technical Engineering
Feb 17, 2025 · Artificial Intelligence

Prompt Engineering: Definitions, Frameworks, Principles, and Advanced Techniques

The guide defines prompts as structured queries that unlock large‑language‑model abilities, outlines five core frameworks (RTF, Chain‑of‑Thought, RISEN, RODES, Density‑Chain), presents two key principles—clear, delimited instructions and explicit reasoning steps—to reduce hallucinations, and surveys advanced techniques such as zero‑shot, few‑shot, RAG, Tree‑of‑Thought and automatic prompt engineering.

AIRetrieval-Augmented Generationchain-of-thought
0 likes · 29 min read
Prompt Engineering: Definitions, Frameworks, Principles, and Advanced Techniques
DataFunSummit
DataFunSummit
Jan 22, 2025 · Artificial Intelligence

RAG2.0 Engine Design Challenges and Implementation

This article presents a comprehensive overview of the RAG2.0 engine design, covering RAG1.0 limitations, effective chunking methods, accurate retrieval techniques, advanced multimodal processing, hybrid search strategies, database indexing choices, and future directions such as agentic RAG and memory‑enhanced models.

ChunkingHybrid SearchMultimodal
0 likes · 23 min read
RAG2.0 Engine Design Challenges and Implementation
Sohu Tech Products
Sohu Tech Products
Jan 8, 2025 · Artificial Intelligence

Multimodal RAG: Implementation Paths and Development Prospects

The talk outlines Multimodal RAG implementation routes, comparing OCR‑based object recognition, transformer encoder‑decoder encoding, and Visual Language Model processing, explains the ColPali late‑interaction method for multi‑dimensional vector matching, addresses scaling tensors with binarization and reranking, and recommends a hybrid long‑term strategy where VLM excels on abstract imagery while traditional OCR remains valuable.

ColPaliDocument processingMultimodal RAG
0 likes · 10 min read
Multimodal RAG: Implementation Paths and Development Prospects
Baidu Geek Talk
Baidu Geek Talk
Dec 16, 2024 · Artificial Intelligence

AIAPI: Baidu's AI-Native Retrieval System for Large Language Model Applications

AIAPI, Baidu’s AI‑native retrieval platform for large language models, tackles hallucination, slow domain updates, and output opacity by delivering authoritative, timely, full‑content data through a dual‑channel architecture that combines traditional search and RAG, employs reusable ranking, graph‑enhanced data layers, dynamic caching that cuts storage by 70 %, and QueryPlan‑based QoS, achieving markedly higher retrieval quality and a 34 % speed gain with Wenxin 4.0.

AI-Native SystemsAIAPILarge Language Models
0 likes · 12 min read
AIAPI: Baidu's AI-Native Retrieval System for Large Language Model Applications
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 15, 2024 · Artificial Intelligence

What Are the Best Practices for Retrieval‑Augmented Generation (RAG)?

This comprehensive study evaluates various components of Retrieval‑Augmented Generation pipelines—including query classification, chunking, embedding models, vector databases, retrieval, re‑ranking, summarization, and generator fine‑tuning—identifies optimal configurations, and proposes best‑practice guidelines for both performance‑maximizing and efficiency‑balanced RAG systems.

Fine-tuningLLMRAG
0 likes · 17 min read
What Are the Best Practices for Retrieval‑Augmented Generation (RAG)?
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Dec 13, 2024 · Artificial Intelligence

Optimizing Graph RAG: Boosting Global QA with Better Chunking, Prompts, and Entity Extraction

This article presents a comprehensive analysis of Graph RAG, detailing its implementation workflow, step‑by‑step execution guide, four targeted optimization strategies, and experimental validation that demonstrates significant improvements in global and local question answering for industry scenarios.

LLM OptimizationPrompt EngineeringRetrieval-Augmented Generation
0 likes · 18 min read
Optimizing Graph RAG: Boosting Global QA with Better Chunking, Prompts, and Entity Extraction
Baidu Tech Salon
Baidu Tech Salon
Nov 13, 2024 · Industry Insights

Baidu’s iRAG and “Miaoda”: Solving AI Hallucinations and Powering the No‑Code Revolution

At Baidu World 2024, CEO Robin Li unveiled the iRAG retrieval‑augmented image generation model that dramatically reduces hallucinations and introduced the no‑code platform “Miaoda,” showcasing intelligent agents as the next mainstream AI application while highlighting explosive growth in daily model usage.

AIIndustry TrendsIntelligent Agents
0 likes · 11 min read
Baidu’s iRAG and “Miaoda”: Solving AI Hallucinations and Powering the No‑Code Revolution
Tencent Docs Tech Team
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
Architects' Tech Alliance
Architects' Tech Alliance
Nov 12, 2024 · Artificial Intelligence

How Retrieval‑Augmented Generation Boosts Enterprise AI with Intel Optimizations

This article explains the fundamentals of Retrieval‑Augmented Generation (RAG), its four‑step workflow, architecture, and how Intel’s hardware and software optimizations—including vector search, quantized embeddings, and advanced inference extensions—enhance performance, security, and scalability for enterprise LLM applications.

AI InferenceEmbedding QuantizationIntel Optimization
0 likes · 14 min read
How Retrieval‑Augmented Generation Boosts Enterprise AI with Intel Optimizations
DataFunSummit
DataFunSummit
Nov 9, 2024 · Artificial Intelligence

GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments

This article introduces GraphRAG, explains the limitations of traditional RAG, outlines four major challenges (fine‑grained retrieval, global context, similarity vs relevance, and macro‑level reasoning), describes GraphRAG’s graph‑based retrieval strategies, showcases comparative experiments, and presents NebulaGraph’s GenAI Suite and RAG products along with future research directions.

AIGraph DatabasesGraphRAG
0 likes · 16 min read
GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments
Fighter's World
Fighter's World
Oct 26, 2024 · Artificial Intelligence

Key Considerations for Deploying Large Language Models in Cloud Services

The article reflects on Alibaba Cloud's large‑model deployments, outlines four service scenarios, examines three fundamental questions about foundation models, and offers a prioritized roadmap—including prompt engineering, RAG, and organizational changes—to effectively bring LLMs to production.

AI deploymentAlibaba CloudLLMOps
0 likes · 8 min read
Key Considerations for Deploying Large Language Models in Cloud Services
DevOps
DevOps
Oct 8, 2024 · Artificial Intelligence

Top 20+ Retrieval‑Augmented Generation (RAG) Interview Questions and Answers

This article presents over twenty essential Retrieval‑Augmented Generation (RAG) interview questions with detailed answers, covering fundamentals, applications, architecture, training, limitations, ethical considerations, and integration, offering AI enthusiasts and job candidates a comprehensive guide to mastering RAG concepts.

AI InterviewNLPRAG
0 likes · 15 min read
Top 20+ Retrieval‑Augmented Generation (RAG) Interview Questions and Answers
Baobao Algorithm Notes
Baobao Algorithm Notes
Sep 10, 2024 · Artificial Intelligence

Do LLMs Silence Human Voices? Unveiling the ‘Spiral of Silence’ in Retrieval‑Augmented Generation

This article reviews the ACL 2024 paper that investigates how large language model‑generated text influences retrieval‑augmented generation pipelines, revealing short‑term retrieval gains but a long‑term “spiral of silence” that marginalizes human‑generated content and homogenizes open‑domain QA results.

AI impactLLMOpen Domain QA
0 likes · 9 min read
Do LLMs Silence Human Voices? Unveiling the ‘Spiral of Silence’ in Retrieval‑Augmented Generation
DataFunSummit
DataFunSummit
Sep 5, 2024 · Artificial Intelligence

NVIDIA’s End‑to‑End Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation

This article introduces NVIDIA’s comprehensive solutions for large language models, covering the NeMo Framework’s full‑stack development pipeline, the open‑source TensorRT‑LLM inference accelerator, and Retrieval‑Augmented Generation techniques, while detailing data preprocessing, distributed training, model fine‑tuning, deployment, and performance optimizations.

Large Language ModelsNeMo FrameworkNvidia
0 likes · 16 min read
NVIDIA’s End‑to‑End Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation
AI Large Model Application Practice
AI Large Model Application Practice
Sep 4, 2024 · Artificial Intelligence

When to Use GraphRAG vs. Traditional RAG and How to Combine Them

This article compares GraphRAG with traditional RAG across seven dimensions—suitable scenarios, knowledge representation, retrieval, comprehensive queries, hidden‑relationship understanding, scalability, and performance‑cost trade‑offs—explains how they can be fused, and offers guidance on selecting the right approach for complex data‑driven applications.

Artificial IntelligenceGraphRAGLLM
0 likes · 13 min read
When to Use GraphRAG vs. Traditional RAG and How to Combine Them
Baidu Geek Talk
Baidu Geek Talk
Sep 2, 2024 · Industry Insights

How a R&D Data Platform Leverages Large Language Models to Accelerate Issue Diagnosis

The article explains how the R&D data middle platform integrates large language models to automate data collection, real‑time monitoring, intelligent analysis, and rapid root‑cause identification for online issues, detailing the architecture, wide‑table modeling, generative BI, attribution algorithms, RAG enhancements, and future optimization plans.

Large Language ModelsRetrieval-Augmented Generationdata platform
0 likes · 37 min read
How a R&D Data Platform Leverages Large Language Models to Accelerate Issue Diagnosis
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 19, 2024 · Artificial Intelligence

How Long‑Tail Knowledge Boosts Retrieval‑Augmented Large Language Models

The paper introduces a method that classifies user queries into ordinary and long‑tail types, applying retrieval‑augmented generation only to long‑tail queries, which improves large language model efficiency and accuracy by leveraging specialized knowledge detection metrics and an extended RAG pipeline.

AI researchECE metricRetrieval-Augmented Generation
0 likes · 9 min read
How Long‑Tail Knowledge Boosts Retrieval‑Augmented Large Language Models
DaTaobao Tech
DaTaobao Tech
Aug 12, 2024 · Artificial Intelligence

Challenges and Optimization Techniques for Retrieval‑Augmented Generation (RAG)

Deploying large language models faces domain gaps, hallucinations, and high barriers, so Retrieval‑Augmented Generation (RAG) combines retrieval with generation, and advanced optimizations—such as RAPTOR’s hierarchical clustering, Self‑RAG’s self‑reflective retrieval, CRAG’s corrective evaluator, proposition‑level Dense X Retrieval, sophisticated chunking, query rewriting, and hybrid sparse‑dense methods—are essential for improving accuracy, reducing hallucinations, and achieving efficient, scalable performance.

AILarge Language ModelsOptimization
0 likes · 22 min read
Challenges and Optimization Techniques for Retrieval‑Augmented Generation (RAG)
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 11, 2024 · Artificial Intelligence

Alibaba Cloud PAI’s Breakthroughs in Chinese Diffusion, Prompting, and LLM Knowledge Editing

Recent ACL 2024 papers from Alibaba Cloud’s PAI platform showcase open‑source Chinese diffusion models, an interactive multi‑turn prompt generator, a long‑tail knowledge‑aware retrieval‑augmented LLM approach, and a dynamic fusion network for sequential model editing, all integrated into cloud services.

AI researchRetrieval-Augmented Generationdiffusion models
0 likes · 11 min read
Alibaba Cloud PAI’s Breakthroughs in Chinese Diffusion, Prompting, and LLM Knowledge Editing
AntTech
AntTech
Aug 6, 2024 · Artificial Intelligence

Trustworthy Alignment of Retrieval‑Augmented Large Language Models via Reinforcement Learning

The article explains how recent research tackles large language model hallucinations by combining retrieval‑augmented generation with reinforcement learning, achieving significant accuracy and reliability gains and paving the way for safe AI deployment in critical sectors such as finance and healthcare.

ICML2024Retrieval-Augmented Generationhallucination
0 likes · 5 min read
Trustworthy Alignment of Retrieval‑Augmented Large Language Models via Reinforcement Learning
JD Tech
JD Tech
Jul 22, 2024 · Artificial Intelligence

Task‑Aware Decoding (TaD): A Plug‑and‑Play Method to Mitigate Hallucinations in Large Language Models

This article presents Task‑aware Decoding (TaD), a plug‑and‑play technique introduced by JD Tech and Tsinghua University and accepted at IJCAI 2024, which reduces intrinsic hallucinations in large language models by comparing pre‑ and post‑fine‑tuning outputs, and demonstrates its effectiveness combined with Retrieval‑Augmented Generation across various tasks.

Fine-tuningLLMRetrieval-Augmented Generation
0 likes · 18 min read
Task‑Aware Decoding (TaD): A Plug‑and‑Play Method to Mitigate Hallucinations in Large Language Models
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Jul 20, 2024 · Artificial Intelligence

Beyond RAG: How Mem0 Gives Large Language Models Super Memory for Personalized AI Apps

Mem0 is an open‑source memory‑management middleware for large language models that provides dynamic, context‑aware, and adaptive memory, outperforming traditional Retrieval‑Augmented Generation (RAG) and enabling personalized AI assistants, travel planners, and support agents with concrete Python APIs and examples.

AI agentsLLMMemory Management
0 likes · 9 min read
Beyond RAG: How Mem0 Gives Large Language Models Super Memory for Personalized AI Apps
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 27, 2024 · Artificial Intelligence

How to Supercharge Retrieval‑Augmented Generation: Papers, Techniques, and Real‑World Tips

This article surveys the main challenges of deploying large language models, introduces key RAG optimization papers such as RAPTOR, Self‑RAG, and CRAG, and compiles practical engineering tricks—including chunking, query rewriting, hybrid and progressive retrieval—to help practitioners build more accurate and efficient RAG systems.

AI researchLLM OptimizationRAG
0 likes · 22 min read
How to Supercharge Retrieval‑Augmented Generation: Papers, Techniques, and Real‑World Tips
JD Tech Talk
JD Tech Talk
Jun 20, 2024 · Artificial Intelligence

Applying Large Language Models to Courier Operations: Intelligent Operations, Q&A, Prompting, and Agents

This article describes how large language models such as ChatGPT are integrated into courier terminal systems to automate tasks, enhance intelligent voice operations, enable retrieval‑augmented question answering, generate smart prompts, and explore agent‑based workflows, supported by code examples for data extraction, splitting, and embedding.

AI for logisticsIntelligent OperationsLarge Language Models
0 likes · 14 min read
Applying Large Language Models to Courier Operations: Intelligent Operations, Q&A, Prompting, and Agents
Sohu Tech Products
Sohu Tech Products
Jun 5, 2024 · Artificial Intelligence

Retrieval Augmented Generation (RAG): Concepts, Workflow, and LangChain Implementation

The article outlines LLM issues such as hallucination, outdated knowledge, and data privacy, then explains Retrieval‑Augmented Generation—detailing its data‑preparation and query‑time retrieval workflow, demonstrates a full LangChain implementation, and contrasts RAG with fine‑tuning as complementary strategies for up‑to‑date, grounded responses.

LLMLangChainPrompt Engineering
0 likes · 15 min read
Retrieval Augmented Generation (RAG): Concepts, Workflow, and LangChain Implementation
DataFunSummit
DataFunSummit
May 16, 2024 · Artificial Intelligence

DataFun Data Science Summit: Cutting‑Edge Research on Causal Inference, Retrieval‑Augmented Generation, and LLM Content Detection

The DataFun Data Science Summit on May 25 brings together leading experts to present cutting‑edge research on pairwise data causal inference, Retrieval‑Augmented Generation applications, large language model content detection, user growth analytics, and advanced machine‑learning techniques across finance, e‑commerce, and AI domains.

AILLM detectionRetrieval-Augmented Generation
0 likes · 14 min read
DataFun Data Science Summit: Cutting‑Edge Research on Causal Inference, Retrieval‑Augmented Generation, and LLM Content Detection
DataFunTalk
DataFunTalk
Mar 14, 2024 · Artificial Intelligence

Efficiency Challenges and Multi‑Layer Optimization for Large AI Models

The article examines how large AI models are moving toward a unified paradigm that reduces task‑algorithm coupling, outlines multi‑layer efficiency challenges—from model compression and sparsity to software and infrastructure optimization—and highlights NVIDIA’s GTC 2024 China AI Day sessions showcasing the latest LLM technologies and registration details.

AI efficiencyMixture of ExpertsNVIDIA GTC
0 likes · 13 min read
Efficiency Challenges and Multi‑Layer Optimization for Large AI Models
Baidu Geek Talk
Baidu Geek Talk
Mar 13, 2024 · Artificial Intelligence

Understanding Retrieval-Augmented Generation (RAG) and Building a Personal Knowledge Base with ERNIE SDK and LangChain

The article explains Retrieval-Augmented Generation (RAG), its workflow, advantages, comparison with fine-tuning, and provides a step-by-step implementation using Baidu's ERNIE SDK, LangChain, and ChromaDB to build a personal knowledge base that answers queries with retrieved context.

AIERNIE SDKKnowledge Base
0 likes · 13 min read
Understanding Retrieval-Augmented Generation (RAG) and Building a Personal Knowledge Base with ERNIE SDK and LangChain
DeWu Technology
DeWu Technology
Jan 22, 2024 · Artificial Intelligence

How to Integrate Business Systems with LLMs: Prompt, RAG, and Fine‑Tuning Strategies

This article outlines three practical approaches—direct prompting, retrieval‑augmented generation (RAG), and fine‑tuning—to connect enterprise applications to large language models, explains key prompt‑engineering techniques, details RAG workflow and vector‑database integration, and provides step‑by‑step guidance for fine‑tuning on the KubeAI platform.

AI for businessFine-tuningKubeAI
0 likes · 20 min read
How to Integrate Business Systems with LLMs: Prompt, RAG, and Fine‑Tuning Strategies
Tencent Cloud Developer
Tencent Cloud Developer
Nov 8, 2023 · Artificial Intelligence

Comprehensive Overview of AI Agents: Concepts, Technical Frameworks, and Applications

The article surveys modern AI agents—software entities powered by large language models that perceive multimodal inputs, reason via brain modules, act through tools or embodied actions, employ retrieval‑augmented generation and chain‑of‑thought planning, and can operate singly (e.g., AutoGPT) or collaboratively via frameworks like Microsoft’s AutoGen—while highlighting current challenges such as controllability, memory limits, parallelism, and reliability.

AI agentsAgent ArchitectureAutoGen
0 likes · 34 min read
Comprehensive Overview of AI Agents: Concepts, Technical Frameworks, and Applications
Baidu Tech Salon
Baidu Tech Salon
Oct 25, 2023 · Artificial Intelligence

Intelligent Question Answering Technology in Baidu Search: Development, Modeling, and Retrieval‑Enhanced Generation

The article surveys Baidu Search’s intelligent question‑answering system, tracing its evolution from feature‑engineered retrieval to large pre‑trained and generative models, and detailing hierarchical readers, multi‑teacher distillation, retrieval‑enhanced generation, and instruction decomposition as key techniques for delivering fast, accurate, citation‑rich answers.

Baidu SearchLarge Language ModelsRetrieval-Augmented Generation
0 likes · 18 min read
Intelligent Question Answering Technology in Baidu Search: Development, Modeling, and Retrieval‑Enhanced Generation
Baidu Geek Talk
Baidu Geek Talk
Oct 25, 2023 · Artificial Intelligence

How Baidu Search Is Transforming Machine Question Answering with Large‑Scale AI Models

This article reviews the evolution of machine question answering, from early feature‑engineered systems to modern large‑language‑model‑driven retrieval‑augmented generation, outlines Baidu Search’s current Retriever‑Reader architecture, discusses challenges such as semantic complexity, latency and answer quality, and presents solutions including hierarchical DocMRC modeling, multi‑teacher knowledge distillation, and instruction decomposition for efficient, high‑quality answers.

BaiduLarge Language ModelsRetrieval-Augmented Generation
0 likes · 18 min read
How Baidu Search Is Transforming Machine Question Answering with Large‑Scale AI Models
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 19, 2023 · Artificial Intelligence

How to Build a Retrieval‑Augmented LLM Knowledge Base on Alibaba Cloud

This guide details a complete end‑to‑end solution for constructing a large‑language‑model knowledge‑base chatbot on Alibaba Cloud, covering background, modular architecture, vector database selection, text preprocessing, embedding models, LLM fine‑tuning, prompt engineering, deployment with PAI‑EAS and BladeLLM, and real‑world results.

AILLMLangChain
0 likes · 37 min read
How to Build a Retrieval‑Augmented LLM Knowledge Base on Alibaba Cloud
dbaplus Community
dbaplus Community
Oct 14, 2023 · Artificial Intelligence

Demystifying Retrieval‑Augmented Generation: From Theory to Working Chatbot

This guide explains the Retrieval‑Augmented Generation (RAG) technique, detailing how user queries are matched to private knowledge bases, how relevant passages are retrieved, and how large language models use those passages to generate context‑aware answers, complete with code examples and practical tips.

ChatbotEmbeddingLLM
0 likes · 19 min read
Demystifying Retrieval‑Augmented Generation: From Theory to Working Chatbot
DataFunSummit
DataFunSummit
Sep 19, 2023 · Artificial Intelligence

Advances in Information Extraction: From PLM to LLM Paradigms at Alibaba DAMO Academy

This article reviews Alibaba DAMO Academy's research on information extraction, covering background concepts, PLM-era extraction paradigms, few‑shot extraction techniques, and the emerging LLM‑era approaches, while also sharing practical insights, benchmark results, and future directions.

Alibaba DAMOLarge Language ModelsRetrieval-Augmented Generation
0 likes · 24 min read
Advances in Information Extraction: From PLM to LLM Paradigms at Alibaba DAMO Academy
phodal
phodal
Sep 17, 2023 · Artificial Intelligence

How Chocolate Factory’s Codebase AI Assistant Boosts Code Search with RAG

This article explains the design and implementation of the Codebase AI Assistant in the Chocolate Factory framework, covering its problem‑solving DSL, retrieval‑augmented generation pipeline, indexing and querying stages, prompt strategies, and code‑splitting rules that together enable efficient semantic code search.

AI assistantCode searchKotlin
0 likes · 11 min read
How Chocolate Factory’s Codebase AI Assistant Boosts Code Search with RAG