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RAG

1179 articles · Page 8 of 12
DataFunSummit
DataFunSummit
Sep 14, 2025 · Artificial Intelligence

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

This article examines Tencent's large language model deployments across various business scenarios, detailing the use of Retrieval‑Augmented Generation, GraphRAG for role‑playing, and Agent technologies, while also outlining core application areas and the three main technical approaches—SFT, RAG, and Agents.

AI ApplicationsAI agentsGraphRAG
0 likes · 4 min read
How Tencent’s Large Language Models Boost Business with RAG, GraphRAG, and AI Agents
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Sep 13, 2025 · Artificial Intelligence

Choosing the Right Path for RAG Development: Low‑Code Platforms vs Open‑Source Frameworks

This article compares low‑code development platforms with open‑source large‑model frameworks such as LangChain and LlamaIndex, outlining their features, advantages, limitations, and suitability for building retrieval‑augmented generation (RAG) applications in various enterprise scenarios.

AI developmentLangChainLow-code
0 likes · 13 min read
Choosing the Right Path for RAG Development: Low‑Code Platforms vs Open‑Source Frameworks
Fun with Large Models
Fun with Large Models
Sep 12, 2025 · Artificial Intelligence

When to Choose Model Fine‑Tuning vs RAG for Large‑Model Engineering Interviews

The article explains the technical background and suitable scenarios for Retrieval‑Augmented Generation (RAG) and model fine‑tuning, compares their strengths, discusses how they can be combined, and provides interview‑style Q&A on their capabilities, risks, and differences from model distillation.

AI InterviewFine‑TuningLarge Language Models
0 likes · 7 min read
When to Choose Model Fine‑Tuning vs RAG for Large‑Model Engineering Interviews
Data Party THU
Data Party THU
Sep 11, 2025 · Artificial Intelligence

How ComRAG Revolutionizes Real‑Time Community QA with Dynamic Vector Stores

ComRAG tackles the static‑knowledge gaps, uneven QA quality, and storage explosion of community question‑answer platforms by integrating a static documentation vector store with dual dynamic CQA stores managed via a centroid‑based memory, delivering higher accuracy, lower latency, and scalable storage for industrial retrieval‑augmented generation.

Artificial IntelligenceCommunity QADynamic Retrieval
0 likes · 7 min read
How ComRAG Revolutionizes Real‑Time Community QA with Dynamic Vector Stores
Instant Consumer Technology Team
Instant Consumer Technology Team
Sep 11, 2025 · Artificial Intelligence

How REFRAG Cuts LLM Decoding Time by 30×: A New Efficient RAG Framework

REFRAG (REpresentation For RAG) introduces a novel decoding framework that compresses, senses, and expands context using precomputed chunk embeddings, achieving up to 30.85× faster first-token generation and 16× larger context windows without sacrificing perplexity, as validated across diverse long‑context tasks.

LLMRAGchunk embeddings
0 likes · 18 min read
How REFRAG Cuts LLM Decoding Time by 30×: A New Efficient RAG Framework
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 11, 2025 · Artificial Intelligence

Building Scalable Enterprise RAG: Lessons, Pitfalls, and Proven Solutions

This article shares practical lessons from building a large‑scale enterprise RAG system, covering imperfect data, document quality scoring, hierarchical chunking, metadata design, semantic‑search failures, open‑source model choices, and table handling to achieve reliable AI‑driven search.

Enterprise AIMetadataOpen-source Models
0 likes · 13 min read
Building Scalable Enterprise RAG: Lessons, Pitfalls, and Proven Solutions
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Sep 11, 2025 · Artificial Intelligence

How AST Boosts LLM‑Powered Code Question Answering: Theory, Practice, and Future Directions

This article explores how abstract syntax trees (AST) can enrich large language model (LLM) based code question‑answering by providing precise structural context, detailing LLM strengths and limits, describing AST‑LLM collaboration, RAG integration, cutting‑edge models, practical tooling, challenges, standardisation efforts, and future research avenues.

ASTLLMRAG
0 likes · 30 min read
How AST Boosts LLM‑Powered Code Question Answering: Theory, Practice, and Future Directions
DaTaobao Tech
DaTaobao Tech
Sep 10, 2025 · Frontend Development

How AI Cut Front‑End Development Time by 60% in Alibaba’s Giraffe Search

This article details how the author transformed a constrained Weex/Muise front‑end project for the “giraffe” search page into an AI‑driven workflow, building a structured knowledge base, defining project‑level rules, and using RAG techniques to accelerate component, tracking, and payment integration, ultimately reducing development time by 60% and proposing a new “AI programming as context engineering” paradigm.

AIKnowledge BaseMuise
0 likes · 14 min read
How AI Cut Front‑End Development Time by 60% in Alibaba’s Giraffe Search
DataFunTalk
DataFunTalk
Sep 10, 2025 · Artificial Intelligence

Why RAG is Evolving: From Retrieval to Integrated Reasoning, Memory, and Multimodal AI

This article explores how Retrieval‑Augmented Generation (RAG) is transitioning from basic retrieve‑and‑generate pipelines to a unified architecture that incorporates reasoning chains, agent layers, knowledge graphs, Monte‑Carlo Tree Search, reinforcement learning, sophisticated memory management, and multimodal tensor‑based retrieval, while addressing engineering challenges such as storage expansion, re‑ranking, and index dimensionality.

AI reasoningRAGRetrieval-Augmented Generation
0 likes · 19 min read
Why RAG is Evolving: From Retrieval to Integrated Reasoning, Memory, and Multimodal AI
Architecture Breakthrough
Architecture Breakthrough
Sep 7, 2025 · Industry Insights

Why Arrogance Blocks You From Riding the AI Wave—and How to Overcome It

The article argues that arrogance, not lack of knowledge, hinders individuals from seizing AI opportunities, outlines four psychological barriers—unseen, undervalued, incomprehensible, and too late—and provides practical steps such as prompt engineering, RAG, fine‑tuning, and AI agents to actively engage with the AI wave.

AIIndustry InsightsPrompt Engineering
0 likes · 11 min read
Why Arrogance Blocks You From Riding the AI Wave—and How to Overcome It
Data Party THU
Data Party THU
Sep 5, 2025 · Artificial Intelligence

What a PRISMA Review Uncovers About Retrieval‑Augmented Generation (RAG)

This systematic PRISMA review analyzes 128 highly‑cited RAG papers, covering five major databases, 343 datasets, a detailed technical roadmap, evaluation metrics from EM to LLM‑as‑Judge, and future research directions, showing that RAG has evolved into a complex, programmable, and auditable distributed system.

AIRAGSystematic Review
0 likes · 5 min read
What a PRISMA Review Uncovers About Retrieval‑Augmented Generation (RAG)
Amazon Cloud Developers
Amazon Cloud Developers
Sep 5, 2025 · Artificial Intelligence

Cut Search Time by 30% and Boost Accuracy 80% with Amazon Bedrock for Financial Data Retrieval

Amazon Finance built an AI assistant that combines Amazon Bedrock, Claude 3 Sonnet, and Amazon Kendra to let analysts query financial data in natural language, achieving a 30% reduction in search time, an 80% increase in accuracy, and high precision and recall across data‑discovery and document‑search tasks.

AI assistantAmazon BedrockAmazon Kendra
0 likes · 20 min read
Cut Search Time by 30% and Boost Accuracy 80% with Amazon Bedrock for Financial Data Retrieval
Instant Consumer Technology Team
Instant Consumer Technology Team
Sep 5, 2025 · Artificial Intelligence

How Context Engineering Transforms Dify Agents: Boost Efficiency by 10×

This article explains how Context Engineering (CE) extends Prompt Engineering by integrating seven core elements—system prompts, user input, short‑term memory, long‑term memory, retrieval, tools, and structured output—using the open‑source Dify platform to build dynamic, multimodal agents that cut inference costs tenfold and raise complex‑task success rates by 40%.

AI Agent DevelopmentDifyLLM
0 likes · 16 min read
How Context Engineering Transforms Dify Agents: Boost Efficiency by 10×
DataFunSummit
DataFunSummit
Sep 4, 2025 · Artificial Intelligence

Unlocking Elasticsearch Vector Search: From Basics to RAG Implementation

This article explores the evolving search demands of the intelligent era, explains dense and sparse vector concepts, details Elasticsearch's vector search capabilities and recent performance breakthroughs, introduces hybrid and relevance‑tuning techniques, and demonstrates RAG principles and real‑world enterprise use cases.

AIElasticsearchHybrid Search
0 likes · 14 min read
Unlocking Elasticsearch Vector Search: From Basics to RAG Implementation
Data Party THU
Data Party THU
Sep 3, 2025 · Artificial Intelligence

Unlocking Large Model Secrets: Transformers, MoE, Fine‑Tuning, RAG & KV Caching

This article provides a comprehensive technical overview of today’s large‑model ecosystem, covering the Transformer architecture, Mixture‑of‑Experts extensions, five fine‑tuning methods, the evolution from traditional RAG to agentic RAG, classic agent design patterns, diverse text‑chunking strategies, and the KV‑cache optimization that accelerates inference.

Fine‑tuningKV cacheMixture of Experts
0 likes · 13 min read
Unlocking Large Model Secrets: Transformers, MoE, Fine‑Tuning, RAG & KV Caching
Efficient Ops
Efficient Ops
Sep 2, 2025 · Artificial Intelligence

How AI Is Revolutionizing Knowledge‑Base Building for Smarter Operations

At the 27th GOPS Global Operations Conference in Shanghai (Oct 17‑18, 2025), Professor Wang Peng of Fudan University will reveal how large language models can extract and structure heterogeneous operational data into high‑quality knowledge bases, and how RAG‑driven Q&A enhances fault diagnosis, SOP generation, and automated decision‑making.

Artificial IntelligenceIntelligent OperationsKnowledge Base
0 likes · 3 min read
How AI Is Revolutionizing Knowledge‑Base Building for Smarter Operations
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 2, 2025 · Artificial Intelligence

Why Enterprise Large‑Model Digitalization Is So Hard: Key Challenges and Capabilities

The article analyzes why enterprise‑wide large‑model AI projects face steep hurdles, outlining required human capabilities, historical labor shifts, current hot technologies such as RAG, Agent, CoT and multimodal, their limits, a three‑stage implementation roadmap, typical case pitfalls, and the key success factors for sustainable digital transformation.

AgentCoTEnterprise AI
0 likes · 15 min read
Why Enterprise Large‑Model Digitalization Is So Hard: Key Challenges and Capabilities
Instant Consumer Technology Team
Instant Consumer Technology Team
Sep 2, 2025 · Artificial Intelligence

Why RAG Is Dead: Jeff Huber’s 5 Retrieval Secrets and Context Engineering

Jeff Huber, founder of Chroma, argues that traditional RAG is obsolete, introduces context engineering as the new paradigm, and shares five practical retrieval strategies, a complete pipeline, and insights on handling context rot, memory, and generative benchmarking to build production‑grade AI applications.

AIContext EngineeringGenerative Benchmarking
0 likes · 11 min read
Why RAG Is Dead: Jeff Huber’s 5 Retrieval Secrets and Context Engineering
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
Data Thinking Notes
Data Thinking Notes
Aug 31, 2025 · Artificial Intelligence

Embedding's Role in Retrieval‑Augmented Generation: Basics, Challenges & Future

This article explains how embedding technology converts unstructured data into vector representations, powers precise retrieval in Retrieval‑Augmented Generation (RAG), outlines the evolution of embedding models, discusses current challenges such as long‑text handling and domain adaptation, and highlights emerging solutions.

AIEmbeddingRAG
0 likes · 12 min read
Embedding's Role in Retrieval‑Augmented Generation: Basics, Challenges & Future
Xiaolei Talks DB
Xiaolei Talks DB
Aug 28, 2025 · Databases

How AI Is Transforming Databases: Highlights from China’s DTCC2025

At DTCC2025 in Beijing, industry leaders showcased AI-driven innovations, vector database advances, RAG techniques, and distributed database performance breakthroughs, illustrating how databases are evolving from passive data stores into intelligent, autonomous systems that boost efficiency, scalability, and business value across sectors.

AIDatabasesRAG
0 likes · 10 min read
How AI Is Transforming Databases: Highlights from China’s DTCC2025
Data Thinking Notes
Data Thinking Notes
Aug 26, 2025 · Artificial Intelligence

From Prompt to Context: How AI Agents Evolve into Proactive Intelligence

This article explores the rapid growth of large language models and explains how AI agents transform passive, single‑turn responses into proactive, continuous intelligence by leveraging a core “Prompt→Context→Action” loop, detailing their architecture, key components, challenges, and future directions.

AI AgentContext ManagementLLM architecture
0 likes · 20 min read
From Prompt to Context: How AI Agents Evolve into Proactive Intelligence
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
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.

DifyIntegrationKnowledge Retrieval
0 likes · 13 min read
Boost Dify’s RAG Performance with Higress AI Gateway: Two Integration Strategies
DataFunSummit
DataFunSummit
Aug 25, 2025 · Artificial Intelligence

Building Xiaomi’s Vertical Domain QA Agent: From RAG to Real‑World Deployment

This article explains how Xiaomi designed and deployed a vertical‑domain question‑answering assistant for product and car queries, covering business background, a four‑module RAG‑plus‑LLM architecture, knowledge‑base construction, custom chunking strategies, dynamic signal handling, and the challenges overcome to achieve reliable real‑time voice interactions.

Agent ArchitectureLLMRAG
0 likes · 22 min read
Building Xiaomi’s Vertical Domain QA Agent: From RAG to Real‑World Deployment
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
Fun with Large Models
Fun with Large Models
Aug 22, 2025 · Artificial Intelligence

Step‑by‑Step Guide: Building a PDF‑Based RAG Knowledge Base with LangChain, Streamlit, DashScope & DeepSeek

This tutorial shows how to create a lightweight Retrieval‑Augmented Generation (RAG) system that indexes multiple PDF files, stores their embeddings in a FAISS vector database, and answers user queries through a LangChain agent powered by DashScope embeddings and the DeepSeek‑Chat model, all wrapped in a Streamlit UI.

DashScopeDeepSeekFAISS
0 likes · 13 min read
Step‑by‑Step Guide: Building a PDF‑Based RAG Knowledge Base with LangChain, Streamlit, DashScope & DeepSeek
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
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 21, 2025 · Artificial Intelligence

Why Your AI Defect Deduplication Returns Mixed Data and How to Fix It

This article details the challenges of building an AI‑powered defect deduplication system using Retrieval‑Augmented Generation, explains why LLMs produce composite (spliced) results, diagnoses the root cause as information loss in the RAG pipeline, and presents a step‑by‑step solution that restores atomicity of records for reliable duplicate detection.

AI debuggingKnowledge BaseLLM
0 likes · 14 min read
Why Your AI Defect Deduplication Returns Mixed Data and How to Fix It
JD Retail Technology
JD Retail Technology
Aug 20, 2025 · Artificial Intelligence

Launch Multi-Agent AI Systems with OxyGent in Just 20 Lines of Code

Learn how to quickly set up OxyGent, a flexible AI agent framework, by installing Python, Node.js, and the MCP tools, configuring environment variables, and using just 20 lines of code to build, debug, and deploy multi‑agent applications with features like RAG, tool integration, and distributed execution.

MCPOxyGentRAG
0 likes · 5 min read
Launch Multi-Agent AI Systems with OxyGent in Just 20 Lines of Code
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 20, 2025 · Artificial Intelligence

How DeepSearch Elevates RAG: From RAG 1.0 to a Multi‑Agent AI Search Engine

This article explains how Alibaba Cloud OpenSearch LLM version evolved from RAG 1.0 to RAG 2.0, introducing the DeepSearch multi‑agent architecture that combines offline data processing, online query handling, planning, clarification, search, and summarization agents to deliver more accurate and complex AI‑driven answers.

AI SearchDeepSearchLLM
0 likes · 10 min read
How DeepSearch Elevates RAG: From RAG 1.0 to a Multi‑Agent AI Search Engine
Instant Consumer Technology Team
Instant Consumer Technology Team
Aug 20, 2025 · Backend Development

How I Built a Production‑Ready RAG Service in 3 Weeks Using AI Coding Tools

In just three weeks, I single‑handedly created a production‑grade Retrieval‑Augmented Generation (RAG) API with FastAPI, leveraging Cursor and Claude Code to automate coding, testing, and deployment, and I share practical insights on AI‑assisted development, high cohesion‑low coupling design, TDD, git worktree parallelism, and agent orchestration.

AI codingGit worktreeRAG
0 likes · 19 min read
How I Built a Production‑Ready RAG Service in 3 Weeks Using AI Coding Tools
Instant Consumer Technology Team
Instant Consumer Technology Team
Aug 19, 2025 · Artificial Intelligence

Mastering Document Chunking for RAG: Strategies, Code & Best Practices

This article explores why proper document chunking is crucial for Retrieval‑Augmented Generation, explains core concepts like context windows and signal‑to‑noise, compares various chunking strategies—from simple fixed‑size splits to semantic and hybrid approaches—and provides practical Python code examples to help you build more effective RAG pipelines.

LLMRAGRetrieval
0 likes · 24 min read
Mastering Document Chunking for RAG: Strategies, Code & Best Practices
Data Thinking Notes
Data Thinking Notes
Aug 17, 2025 · Artificial Intelligence

Unlocking AI Agents: From Basics to Real-World Development

This article provides a comprehensive overview of AI Agents, covering their fundamental concepts, core features, technical evolution, work cycle, architectural modules, key technologies such as prompt engineering and RAG, practical development steps, a data‑analysis agent case study, and typical industry applications.

AI AgentAgent ArchitectureArtificial Intelligence
0 likes · 13 min read
Unlocking AI Agents: From Basics to Real-World Development
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 15, 2025 · Artificial Intelligence

Mastering AI Agents: Prompt Engineering, Workflows, and RAG Strategies

This article systematically explains how to build reliable, high‑performance AI agents by focusing on the core components—LLM, prompts, workflows, RAG, and tools—while covering prompt engineering techniques, DSL‑based workflow design, vector‑database knowledge bases, security against prompt injection, and practical project planning.

AI AgentLLMRAG
0 likes · 15 min read
Mastering AI Agents: Prompt Engineering, Workflows, and RAG Strategies
Tencent Technical Engineering
Tencent Technical Engineering
Aug 14, 2025 · Artificial Intelligence

Why Do Large Language Models Hallucinate? Causes, Risks, and Multi‑Dimensional Solutions

This article systematically examines the root causes of hallucinations in large language models, evaluates their pros and cons, and presents a comprehensive set of optimization techniques—including prompt engineering, RAG, sampling tweaks, supervised fine‑tuning, LoRA, RLHF, chain‑of‑thought reasoning, and agent/workflow designs—to build more reliable and trustworthy AI applications.

AILLMLoRA
0 likes · 29 min read
Why Do Large Language Models Hallucinate? Causes, Risks, and Multi‑Dimensional Solutions
DaTaobao Tech
DaTaobao Tech
Aug 13, 2025 · Artificial Intelligence

Unlocking AI Power: A Complete Guide to Prompt Engineering and Advanced Techniques

This article explores the emerging field of prompt engineering, detailing its fundamentals, advanced strategies such as chain‑of‑thought, ReAct, and structured frameworks, and demonstrates practical applications in AI agents for data retrieval, SQL generation, and market insight, offering actionable guidance for developers and business users alike.

AI agentsData RetrievalRAG
0 likes · 42 min read
Unlocking AI Power: A Complete Guide to Prompt Engineering and Advanced Techniques
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 11, 2025 · Artificial Intelligence

How Fine‑Tuning Large Models Solves Code Upgrade Challenges and Boosts Stable Module Matching

This article details an innovative approach that uses large‑model supervised fine‑tuning to overcome the instability of code RAG and code agents during open‑source repository upgrades, addressing domain‑specific terminology, code style differences, and improving recall, accuracy, and deployment efficiency.

AI agentsFine-tuningLLM
0 likes · 11 min read
How Fine‑Tuning Large Models Solves Code Upgrade Challenges and Boosts Stable Module Matching
AI Large Model Application Practice
AI Large Model Application Practice
Aug 11, 2025 · Artificial Intelligence

How to Build an LLM-Powered Smart Resume Screening System

This article presents a detailed design and implementation of an LLM‑based intelligent resume matching system that combines semantic vector retrieval, structured rule filtering, multi‑dimensional weighted scoring, and natural‑language interaction to create a fast, quantifiable, and explainable hiring pipeline.

AI recruitmentLLMRAG
0 likes · 18 min read
How to Build an LLM-Powered Smart Resume Screening System
Hailey Says
Hailey Says
Aug 10, 2025 · Artificial Intelligence

Building a MAS‑Powered RAG System for Blog Search and Q&A

This article walks through constructing an agentic RAG pipeline that combines LangChain, LangGraph, Google Gemini embeddings, and Qdrant vector storage to enable automatic query rewriting, relevance grading, and concise answers to blog‑post questions via a Streamlit UI.

Google GeminiLangChainLangGraph
0 likes · 9 min read
Building a MAS‑Powered RAG System for Blog Search and Q&A
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 8, 2025 · Artificial Intelligence

Can GitOps Power Low‑Cost LLM Agents? A Hands‑On Exploration

This article examines how the Manus sandbox and CodeAct mechanisms inspire a GitOps‑based approach to building LLM agents, detailing the design of planner and executor components, workflow steps, advantages such as RAG and observability, and the potential for low‑cost, scalable intelligent agent development.

AI agentsGitOpsIntelligent Agents
0 likes · 12 min read
Can GitOps Power Low‑Cost LLM Agents? A Hands‑On Exploration
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 8, 2025 · Artificial Intelligence

Unlocking Big Data Ops with Large Models: Opportunities, Challenges, Design

This article summarizes a Cloud Summit talk where Alibaba Cloud’s AI expert Zhang Yingying explains how large language models can enhance big‑data intelligent operations, covering opportunities, challenges, RAG‑based Q&A, multi‑agent diagnostics, and the engineering architecture needed for reliable, scalable deployment.

AI EngineeringBig Data OperationsLarge Language Models
0 likes · 20 min read
Unlocking Big Data Ops with Large Models: Opportunities, Challenges, Design
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 8, 2025 · Artificial Intelligence

What Von Neumann’s Brain Theory Reveals About Prompt Engineering for LLMs

The article explores how Von Neumann’s insights on the brain‑computer analogy illuminate modern large‑language‑model prompt engineering, comparing logical reasoning chains, memory mechanisms, and DSL‑driven computation to improve accuracy, reduce hallucinations, and balance reasoning depth with precise calculation.

Large Language ModelsPrompt EngineeringRAG
0 likes · 14 min read
What Von Neumann’s Brain Theory Reveals About Prompt Engineering for LLMs
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Aug 8, 2025 · Industry Insights

How CodeRAG Reinvents Large‑Scale Code Repository Knowledge Extraction and Hierarchical Retrieval

CodeRAG leverages AST‑centric parsing and a hierarchical knowledge graph to overcome text‑only retrieval limits in large code repositories, offering multi‑language analysis, incremental parsing, hybrid indexing, and intelligent context selection for tasks such as code completion, Q&A, documentation generation, and impact analysis.

ASTCodeRAGLarge-Scale Repos
0 likes · 15 min read
How CodeRAG Reinvents Large‑Scale Code Repository Knowledge Extraction and Hierarchical Retrieval
Ubiquitous Tech
Ubiquitous Tech
Aug 7, 2025 · Artificial Intelligence

How to Use RAG Tool Calls in FastGPT for Real-Time Train Ticket Queries

This article walks through integrating RAG‑based tool calling into a FastGPT workflow to enable AI‑driven customer service that can fetch real‑time train ticket information by invoking external APIs, configuring HTTP nodes, and handling parameters step by step.

AI Customer ServiceFastGPTHTTP API
0 likes · 18 min read
How to Use RAG Tool Calls in FastGPT for Real-Time Train Ticket Queries
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 5, 2025 · Databases

How PolarDB IMCI Unifies Vector Search and Embedding in One SQL Engine

This article explains how PolarDB IMCI integrates vector indexing and embedding directly into the database kernel, offering a unified, transactional, and real‑time vector lifecycle management service that lets developers build RAG knowledge bases and AI applications using only standard SQL, dramatically reducing development and operational complexity.

AIPolarDBRAG
0 likes · 11 min read
How PolarDB IMCI Unifies Vector Search and Embedding in One SQL Engine
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 5, 2025 · Artificial Intelligence

Mastering Intent Detection & Slot Filling: Proven Strategies and Code Samples

This article shares reusable AI development techniques for intent detection and slot filling, comparing four solution tiers—from simple prompt engineering to advanced RAG‑enhanced architectures—complete with practical code snippets, performance trade‑offs, and guidance on selecting the optimal approach for reliable conversational agents.

Intent DetectionNLUPrompt Engineering
0 likes · 27 min read
Mastering Intent Detection & Slot Filling: Proven Strategies and Code Samples
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Aug 4, 2025 · Artificial Intelligence

How RAG and Long‑Term Memory Turn AI into a Truly Remembering Assistant

This article explains how Retrieval‑Augmented Generation (RAG) and long‑term memory systems like MenoBase enable large language models to overcome short‑term memory limits, dynamically retrieve up‑to‑date knowledge, and personalize interactions, with practical Dify implementation steps and real‑world use cases across industries.

AIDifyKnowledge Base
0 likes · 18 min read
How RAG and Long‑Term Memory Turn AI into a Truly Remembering Assistant
Hailey Says
Hailey Says
Aug 3, 2025 · Artificial Intelligence

The 5W1H of Context Engineering: A Method for Agentic AI

This article defines Context Engineering (CE), contrasts it with traditional Prompt Engineering, explains its components and benefits for Agentic AI, outlines practical steps and best‑practice techniques—including KV‑cache design, masking, file‑system context, and attention manipulation—while also discussing evaluation challenges and future outlook.

Context EngineeringKV cacheLLM
0 likes · 18 min read
The 5W1H of Context Engineering: A Method for Agentic AI
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
Ops Development Stories
Ops Development Stories
Jul 29, 2025 · Artificial Intelligence

Master AI Agents with LangGraph: Build Adaptive RAG, Translation, and ReAct Agents

This comprehensive guide explains what an AI Agent is, its core capabilities and design patterns, and walks through step‑by‑step implementations of RAG, Translation, and ReAct agents using LangGraph, complete with code samples, workflow diagrams, and practical tips for building personal ops knowledge‑base agents.

LLMLangGraphRAG
0 likes · 64 min read
Master AI Agents with LangGraph: Build Adaptive RAG, Translation, and ReAct Agents
SF Technology Team
SF Technology Team
Jul 29, 2025 · Artificial Intelligence

How SF Tech’s Proprietary Large Models Revolutionize Logistics and AI Operations

The DA Data Intelligence Conference in Shenzhen showcased SF Tech’s breakthroughs in large‑model AI, revealing how their proprietary multimodal models, RAG innovations, and agent platforms dramatically improve logistics decision‑making, resource scheduling, and customer service across multiple industries.

AI operationsAgent PlatformRAG
0 likes · 11 min read
How SF Tech’s Proprietary Large Models Revolutionize Logistics and AI Operations
Architecture and Beyond
Architecture and Beyond
Jul 27, 2025 · Artificial Intelligence

What Makes an AI Agent Tick? From Expert Systems to Modern Architectures

This article traces the evolution of AI agents from early expert systems to today’s multimodal, memory‑rich agents, explains their perception, reasoning, memory and action modules, discusses model selection, prompt engineering, RAG techniques, and highlights current limitations such as hallucinations, reliability, cost, and security.

AI AgentFunction CallingMemory Architecture
0 likes · 28 min read
What Makes an AI Agent Tick? From Expert Systems to Modern Architectures
JD Tech Talk
JD Tech Talk
Jul 23, 2025 · Artificial Intelligence

Causal Inference + LLMs: Transforming E‑Commerce Pricing Strategies

This article describes how integrating causal inference with large language models and Retrieval‑Augmented Generation can automate and explain e‑commerce product pricing, detailing the three‑step workflow, reinforcement‑learning rewards, experimental results, and future directions for end‑to‑end RAG‑LLM training.

RAGcausal inferencee‑commerce pricing
0 likes · 15 min read
Causal Inference + LLMs: Transforming E‑Commerce Pricing Strategies
Zhuanzhuan Tech
Zhuanzhuan Tech
Jul 23, 2025 · Artificial Intelligence

Why AI‑Generated Code Often Misses the Mark and How a Code Knowledge Base Fixes It

AI‑generated code frequently fails to match project conventions due to lack of contextual memory, but building a dynamic code knowledge base combined with Retrieval‑Augmented Generation (RAG) enables precise, compliant code output, reduces errors, accelerates development, and transforms AI into a project‑specific assistant.

AIKnowledge BaseRAG
0 likes · 13 min read
Why AI‑Generated Code Often Misses the Mark and How a Code Knowledge Base Fixes It
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
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Jul 18, 2025 · Artificial Intelligence

Video: Building an Intelligent Knowledge‑Base Q&A System with Large Models and Elasticsearch (RAG)

The video walks through the differences between traditional keyword search and vector search, explains the core concept of Retrieval‑Augmented Generation, and demonstrates how to construct a knowledge‑base Q&A system using a large language model integrated with Elasticsearch.

ElasticsearchKnowledge BaseQ&A system
0 likes · 1 min read
Video: Building an Intelligent Knowledge‑Base Q&A System with Large Models and Elasticsearch (RAG)
DataFunSummit
DataFunSummit
Jul 16, 2025 · Artificial Intelligence

How Tencent Cloud ES Powers RAG with Hybrid Search and Massive Vector Optimizations

This article explores how Tencent Cloud Elasticsearch combines decades of text search expertise with cutting‑edge vector retrieval and large language models to deliver a one‑stop Retrieval‑Augmented Generation solution, detailing the underlying models, hybrid search architecture, performance tricks, and real‑world case studies.

ElasticsearchHybrid SearchLLM
0 likes · 24 min read
How Tencent Cloud ES Powers RAG with Hybrid Search and Massive Vector Optimizations
DataFunSummit
DataFunSummit
Jul 15, 2025 · Artificial Intelligence

Unlocking Semantic Search: Elasticsearch Vector Search & RAG Applications

This article explains why traditional keyword search falls short, introduces Elasticsearch's vector search and hybrid retrieval capabilities, and shows how combining it with large language models enables Retrieval‑Augmented Generation (RAG) for more accurate, context‑aware AI-driven search across text and multimedia data.

AIElasticsearchLarge Language Models
0 likes · 5 min read
Unlocking Semantic Search: Elasticsearch Vector Search & RAG Applications
Tencent Cloud Developer
Tencent Cloud Developer
Jul 15, 2025 · Artificial Intelligence

How RAG Evolved: From Naive to Agentic – A Complete Guide

This article systematically outlines the evolution of Retrieval‑Augmented Generation (RAG) from its naive three‑step pipeline to advanced, modular, and agentic architectures, highlighting each generation's motivations, core features, advantages, drawbacks, and practical implementation details for large language model applications.

Agentic RAGArtificial IntelligenceLLM
0 likes · 20 min read
How RAG Evolved: From Naive to Agentic – A Complete Guide
Ubiquitous Tech
Ubiquitous Tech
Jul 14, 2025 · Artificial Intelligence

How to Craft Human‑like RAG Prompts for AI Customer Service

This article explains why prompt engineering is essential for turning large‑language‑model‑driven chatbots into empathetic assistants, outlines a 5W1H framework for designing structured prompts, provides concrete travel‑assistant examples, and shares practical techniques and tools such as Prompt Optimizer to improve RAG reliability.

AI Customer ServiceAI ToolsChatbot
0 likes · 25 min read
How to Craft Human‑like RAG Prompts for AI Customer Service
Ops Development Stories
Ops Development Stories
Jul 14, 2025 · Artificial Intelligence

Mastering AIOps: Prompt Engineering, Function Calling, RAG, Graph RAG, and Local LLM Deployment

This comprehensive guide explores AIOps techniques such as prompt engineering, chat completions, memory management, function calling, fine‑tuning, retrieval‑augmented generation (RAG), graph‑based RAG, and practical steps for deploying open‑source large language models locally, providing code examples and best‑practice recommendations for modern DevOps environments.

AIOpsFunction CallingLocal LLM Deployment
0 likes · 47 min read
Mastering AIOps: Prompt Engineering, Function Calling, RAG, Graph RAG, and Local LLM Deployment
Tencent Technical Engineering
Tencent Technical Engineering
Jul 14, 2025 · Artificial Intelligence

Demystifying AIGC, Agents, and MCP: Core Concepts and How They Interact

This article provides a concise overview of the latest AI concepts—including AIGC, Retrieval‑Augmented Generation, Function‑Calling models, intelligent agents, and the Model Context Protocol—explaining their principles, differences, and how they can be combined to build more powerful AI applications for developers outside the AI field.

AIGCAgentFunction Calling
0 likes · 15 min read
Demystifying AIGC, Agents, and MCP: Core Concepts and How They Interact
DaTaobao Tech
DaTaobao Tech
Jul 14, 2025 · Artificial Intelligence

Mastering AI Application Modes: Embedding, Copilot, and Agents Explained

This article explores practical AI engineering strategies, detailing the three AI application modes—Embedding, Copilot, and Agents—along with prompt engineering, model selection, function calling, RAG, workflow design, and multi‑agent architectures to boost business efficiency and user experience.

AIAgentsModel Evaluation
0 likes · 25 min read
Mastering AI Application Modes: Embedding, Copilot, and Agents Explained
Data Thinking Notes
Data Thinking Notes
Jul 13, 2025 · Artificial Intelligence

How to Build an Enterprise Knowledge Base with Dify: Full Setup Guide

This article walks developers through the entire process of deploying Dify locally, configuring model providers, creating and segmenting a knowledge base with RAG, choosing indexing methods, and integrating the knowledge base into a chatbot application, complete with code snippets and visual guides.

AI deploymentDifyKnowledge Base
0 likes · 11 min read
How to Build an Enterprise Knowledge Base with Dify: Full Setup Guide
Architecture and Beyond
Architecture and Beyond
Jul 12, 2025 · Artificial Intelligence

What Exactly Is an AI Agent? History, Architecture, and Future Challenges

This article traces the evolution of AI agents from early expert systems to modern large‑language‑model‑driven assistants, explains their core perception, reasoning, memory, and action modules, compares thinking and execution models, and discusses current limitations such as hallucinations, reliability, cost, and security.

AI AgentMemory ArchitecturePrompt Engineering
0 likes · 20 min read
What Exactly Is an AI Agent? History, Architecture, and Future Challenges
Architect
Architect
Jul 11, 2025 · Artificial Intelligence

How OpenAI’s Zero‑Vector Agentic RAG Redefines AI Knowledge Retrieval

OpenAI’s new non‑vectorized Agentic RAG approach replaces traditional vector search with a hierarchical, multi‑round content selection process, leveraging large‑context models like GPT‑4.1‑mini for efficient document loading, dynamic navigation, and accurate answer generation, while outlining model selection strategies, cost trade‑offs, and production considerations.

AI architectureAgentic RetrievalModel Selection
0 likes · 15 min read
How OpenAI’s Zero‑Vector Agentic RAG Redefines AI Knowledge Retrieval
Sanyou's Java Diary
Sanyou's Java Diary
Jul 10, 2025 · Artificial Intelligence

Demystifying AIGC, Agents, RAG, and MCP: Core AI Concepts Explained

This article provides a concise overview of the latest AI breakthroughs—including AIGC, multimodal technology, Retrieval‑Augmented Generation (RAG), intelligent agents with function‑calling models, and the Model Context Protocol (MCP)—explaining their principles, relationships, and practical implications for developers outside the AI field.

AIAIGCAgents
0 likes · 16 min read
Demystifying AIGC, Agents, RAG, and MCP: Core AI Concepts Explained
DataFunSummit
DataFunSummit
Jul 10, 2025 · Artificial Intelligence

How Large Language Model AI Agents Transform Intelligent Operations and On‑Call Support

This article details the design and implementation of a large‑model‑driven intelligent operations dialogue system, covering intent recognition, routing, multi‑agent planning, RAG, workflow, ReAct, reflection, tree‑search techniques, evaluation challenges, and future multi‑agent collaboration for on‑call support.

AI agentsIntelligent OperationsRAG
0 likes · 23 min read
How Large Language Model AI Agents Transform Intelligent Operations and On‑Call Support
Tencent Cloud Developer
Tencent Cloud Developer
Jul 10, 2025 · Artificial Intelligence

Demystifying AIGC, Agents, and MCP: Essential AI Concepts for Developers

This article provides a concise, developer‑focused overview of emerging AI concepts—including AIGC, multimodal models, Retrieval‑Augmented Generation, intelligent agents, Function‑Calling, and the Model Context Protocol (MCP)—explaining their core principles, differences, and how they interrelate to enable advanced AI applications.

AIAIGCAgent
0 likes · 16 min read
Demystifying AIGC, Agents, and MCP: Essential AI Concepts for Developers
JD Tech Talk
JD Tech Talk
Jul 8, 2025 · Artificial Intelligence

How AI Can Turn a Code Maze into a Knowledge Highway for New Developers

New developer Li Ming’s frustrating onboarding experience highlights hidden business rules, undocumented code, and poor knowledge transfer, prompting him to build an AI‑driven knowledge base that links code changes, requirements, and operational docs, ultimately streamlining troubleshooting, accelerating feature development, and improving knowledge retention across teams.

AIKnowledge ManagementRAG
0 likes · 18 min read
How AI Can Turn a Code Maze into a Knowledge Highway for New Developers
JD Cloud Developers
JD Cloud Developers
Jul 8, 2025 · Artificial Intelligence

How AI Can Turn a Code Maze into a Knowledge Hub for New Developers

This article follows a new developer named Li Ming as he confronts undocumented code, hidden business rules, and fragmented knowledge, then demonstrates how leveraging large‑language models to index, associate, and retrieve code, requirements, and operational data can create an intelligent knowledge base that streamlines onboarding, reduces errors, and enhances collaboration across development, testing, and product teams.

AIRAGsoftware development
0 likes · 19 min read
How AI Can Turn a Code Maze into a Knowledge Hub for New Developers
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
Instant Consumer Technology Team
Instant Consumer Technology Team
Jul 3, 2025 · Artificial Intelligence

Why Buying an AI Appliance Is a Strategic Pitfall for Enterprises

Enterprises rushing to purchase DeepSeek AI appliances and smart‑agent platforms often face hidden technical, data, and organizational challenges that turn promised "plug‑and‑play" solutions into costly missteps, highlighting the need for realistic strategy, robust data governance, and continuous capability building.

AI capability buildingAI deploymentData Governance
0 likes · 28 min read
Why Buying an AI Appliance Is a Strategic Pitfall for Enterprises
AI Large Model Application Practice
AI Large Model Application Practice
Jul 2, 2025 · Artificial Intelligence

Build a PPT‑Powered RAG Engine with Visual Models and MCP Server

This article explains how to construct a Retrieval‑Augmented Generation (RAG) pipeline for multi‑page PPT documents by converting slides to images, extracting content with a vision model, indexing with LlamaIndex and Chroma, and exposing the functionality through an MCP Server with tools for adding, querying, and managing PPTs.

MCP ServerPPTPython
0 likes · 13 min read
Build a PPT‑Powered RAG Engine with Visual Models and MCP Server
Ops Development Stories
Ops Development Stories
Jul 1, 2025 · Artificial Intelligence

From Lean to AIOps: How AI is Transforming Modern Operations

This comprehensive guide walks through the evolution from Lean and Agile practices to DevOps and finally AIOps, explaining core concepts, key algorithms, the role of large language models, RAG‑based root‑cause analysis, and practical implementation steps for intelligent operations.

AIOpsLarge Language ModelsLean
0 likes · 19 min read
From Lean to AIOps: How AI is Transforming Modern Operations
DataFunTalk
DataFunTalk
Jun 29, 2025 · Artificial Intelligence

Large Models Boost Douyin User Experience: Expert Insights

In an interview at the DA Digital Intelligence Conference, ByteDance AI specialist Cai Conghuai explains how large language models, combined with techniques like SFT, DPO, and RAG, are reshaping Douyin's user‑experience signal detection, root‑cause analysis, and evaluation, while outlining future AI‑agent breakthroughs.

AIDPOLarge Language Models
0 likes · 12 min read
Large Models Boost Douyin User Experience: Expert Insights
Architect
Architect
Jun 28, 2025 · Artificial Intelligence

How MultiAgentPPT Generates Slides with AI Agents: Architecture and Code Walkthrough

This article examines the MultiAgentPPT project, detailing its multi‑agent workflow, the four core agents that generate outlines, split topics, conduct research, and summarize results, and explains how the system retrieves data via a WeChat crawler and constructs prompts for LLM‑driven PPT creation.

AI agentsMultiAgentPPTPPT Generation
0 likes · 6 min read
How MultiAgentPPT Generates Slides with AI Agents: Architecture and Code Walkthrough
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jun 27, 2025 · Artificial Intelligence

Build a Powerful AI Search RAG Application with PAI‑LangStudio, Qwen3 & Elasticsearch

This guide walks you through using the PAI‑LangStudio platform together with the Qwen3 large language model and Elasticsearch to create a full‑stack AI Search RAG solution, covering prerequisites, step‑by‑step configuration of model services, database connections, runtimes, knowledge bases, workflow creation, testing, and deployment for production use.

AI SearchElasticsearchPAI‑LangStudio
0 likes · 11 min read
Build a Powerful AI Search RAG Application with PAI‑LangStudio, Qwen3 & Elasticsearch
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Jun 27, 2025 · Operations

How AI‑Powered Ops‑Nexus Transforms Intelligent Operations for 100k+ Servers

This article details the design, technology choices, functional modules, core implementation, performance optimizations, and future roadmap of Ops‑Nexus, an AI‑driven intelligent operations platform that streamlines alarm analysis, log processing, and host health checks for large‑scale monitoring environments.

AI opsIntelligent OperationsLLM
0 likes · 12 min read
How AI‑Powered Ops‑Nexus Transforms Intelligent Operations for 100k+ Servers
Subtle Storm
Subtle Storm
Jun 26, 2025 · Artificial Intelligence

Why Large Language Models Hallucinate and How to Prevent It

The article explains that AI hallucination stems from probabilistic language modeling, imperfect training data, missing verification mechanisms, and ambiguous user prompts, and it outlines practical countermeasures such as retrieval‑augmented generation, fine‑tuning, temperature control, prompt engineering, and multi‑model voting to reduce fabricated outputs.

AI hallucinationFine-tuningLarge Language Models
0 likes · 8 min read
Why Large Language Models Hallucinate and How to Prevent It