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Youzan Coder
Youzan Coder
May 8, 2025 · Artificial Intelligence

Building and Optimizing a Store Smart Assistant with Aily: Architecture, Workflow, and Practical Lessons

The article details how Youzan’s Store Smart Assistant was built on the Feishu Aily platform, describing why Aily was chosen, the three‑stage development process, deep system integration, practical tips for knowledge‑base management and model stability, and the resulting efficiency gains such as handling 80% of routine queries.

AI AssistantAily platformKnowledge Base
0 likes · 24 min read
Building and Optimizing a Store Smart Assistant with Aily: Architecture, Workflow, and Practical Lessons
ITPUB
ITPUB
Apr 28, 2025 · Artificial Intelligence

How Large Language Models are Transforming Automotive Operations and Optimization

In this interview, an automotive industry expert explains how large language models and advanced operations‑optimization techniques are reshaping vehicle design, production planning, logistics, and customer services, while also discussing implementation challenges, team requirements, and future AI‑driven opportunities.

AI adoptionAutomotive AILarge Language Models
0 likes · 15 min read
How Large Language Models are Transforming Automotive Operations and Optimization
DevOps
DevOps
Apr 27, 2025 · Artificial Intelligence

Large Model Technologies: RAG, AI Agents, Multimodal Applications, and Future Trends

This article examines how Retrieval‑Augmented Generation (RAG), AI agents, and multimodal large‑model techniques are reshaping AI‑industry integration, discusses their technical challenges and practical implementations, and outlines future development directions across algorithms, products, and domain‑specific applications.

AI agentsArtificial IntelligenceLarge Models
0 likes · 14 min read
Large Model Technologies: RAG, AI Agents, Multimodal Applications, and Future Trends
Fun with Large Models
Fun with Large Models
Apr 25, 2025 · Artificial Intelligence

Why Your RAG System Underperforms and How to Boost Its Effectiveness by 20%

This article analyzes common shortcomings of RAG pipelines—data preparation, retrieval, and LLM generation—and provides concrete optimization techniques such as advanced chunking, embedding model selection, retrieval parameter tuning, rerank models, and prompt engineering, promising up to a 20% performance gain.

ChunkingEmbeddingPrompt Engineering
0 likes · 17 min read
Why Your RAG System Underperforms and How to Boost Its Effectiveness by 20%
DataFunTalk
DataFunTalk
Apr 24, 2025 · Artificial Intelligence

Is Retrieval‑Augmented Generation (RAG) Dead Yet?

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

AIKnowledge retrievalRAG
0 likes · 9 min read
Is Retrieval‑Augmented Generation (RAG) Dead Yet?
Tencent Cloud Developer
Tencent Cloud Developer
Apr 24, 2025 · Industry Insights

How RAG, AI Agents, and Multimodal Models Are Reshaping Industry – Trends, Challenges, and Real‑World Cases

The article analyzes the rapid evolution of large‑model technologies—Retrieval‑Augmented Generation, autonomous agents, and multimodal AI—detailing their technical foundations, practical challenges, industry applications such as unified multimodal tasks, open‑world detection, and video moderation, and forecasting future development directions.

AI agentsLarge ModelsRAG
0 likes · 15 min read
How RAG, AI Agents, and Multimodal Models Are Reshaping Industry – Trends, Challenges, and Real‑World Cases
Big Data Technology & Architecture
Big Data Technology & Architecture
Apr 22, 2025 · Artificial Intelligence

Introduction to Retrieval‑Augmented Generation (RAG) and Vector Indexing with StarRocks and DeepSeek

This article explains the fundamentals of Retrieval‑Augmented Generation, demonstrates how to create and query vector indexes using StarRocks, shows how DeepSeek provides embeddings and answer generation, and walks through a complete end‑to‑end RAG pipeline with code examples and a web UI.

AIDeepSeekEmbedding
0 likes · 20 min read
Introduction to Retrieval‑Augmented Generation (RAG) and Vector Indexing with StarRocks and DeepSeek
DevOps
DevOps
Apr 20, 2025 · Artificial Intelligence

Building a Medical Knowledge Base with RAG: A Step‑by‑Step Example

This article demonstrates how to construct an AI‑powered medical knowledge base for diabetes treatment by preprocessing literature, performing semantic chunking, generating BioBERT embeddings, storing them in a FAISS vector database, and using a RAG framework together with a knowledge graph to retrieve and generate accurate answers.

BioBERTFAISSKnowledge Graph
0 likes · 12 min read
Building a Medical Knowledge Base with RAG: A Step‑by‑Step Example
DaTaobao Tech
DaTaobao Tech
Apr 18, 2025 · Frontend Development

How AI Is Transforming Frontend Development: From Design‑to‑Code to Automated Testing

This article explores how AI-driven tools are reshaping frontend engineering by automating design‑to‑code conversion, interface‑to‑data‑model mapping, private component integration, code fitting, AI code review, and automated test regression, and it evaluates their impact on efficiency and future development workflows.

AIAutomationCodeGeneration
0 likes · 37 min read
How AI Is Transforming Frontend Development: From Design‑to‑Code to Automated Testing
Fun with Large Models
Fun with Large Models
Apr 18, 2025 · Artificial Intelligence

How RAG Works: From Data Prep to LLM Generation Explained

This article breaks down Retrieval‑Augmented Generation (RAG) into its three core stages—data preparation, data retrieval, and LLM generation—showing how document chunking, embedding, vector databases, similarity search, and optional re‑ranking combine to let large language models produce more accurate, knowledge‑grounded answers.

EmbeddingLLMRAG
0 likes · 9 min read
How RAG Works: From Data Prep to LLM Generation Explained
Data Thinking Notes
Data Thinking Notes
Apr 17, 2025 · Artificial Intelligence

How Dify Accelerates Generative AI App Development with Low‑Code and Modular Design

Dify is an open‑source LLM application platform that blends BaaS and LLMOps, offering low‑code development, modular components, extensive model support, and advanced retrieval features, while also detailing its current limitations and recent enhancements such as MySQL integration and Elasticsearch‑based RAG capabilities.

AIElasticsearchLLM
0 likes · 7 min read
How Dify Accelerates Generative AI App Development with Low‑Code and Modular Design
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
Apr 10, 2025 · Artificial Intelligence

Build a RAG-Powered Knowledge Base with Spring Boot, Milvus, and Ollama

This guide walks through creating a Retrieval‑Augmented Generation (RAG) system using Spring Boot 3.4.2, Milvus vector database, and the bge‑m3 embedding model via Ollama, covering environment setup, dependency configuration, vector store operations, and integration with a large language model to deliver refined, similarity‑based answers.

EmbeddingLLMMilvus
0 likes · 11 min read
Build a RAG-Powered Knowledge Base with Spring Boot, Milvus, and Ollama
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Apr 10, 2025 · Artificial Intelligence

Building a Pet Hospital AI Assistant with RAG and LLMs

This article walks through the motivation, core concepts of Retrieval‑Augmented Generation, and a step‑by‑step guide to constructing a pet‑hospital AI assistant on Alibaba Cloud using LLMs, vector databases, and automated pipelines, complete with code examples and practical tips.

AI AssistantAlibaba CloudLLM
0 likes · 18 min read
Building a Pet Hospital AI Assistant with RAG and LLMs
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?
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 8, 2025 · Artificial Intelligence

Unlocking LLM Secrets: From Prompt Basics to RAG and Tool Integration

This article introduces the fundamental paradigms of large language models, explaining how simple prompts, messages, and tools like RAG and ReAct enable powerful applications, while providing practical code examples, translation strategies, and insights on prompt engineering, tool usage, and model fine‑tuning.

AILLM applicationsLarge Language Models
0 likes · 23 min read
Unlocking LLM Secrets: From Prompt Basics to RAG and Tool Integration
dbaplus Community
dbaplus Community
Apr 7, 2025 · Databases

How Do LLMs Tackle Oracle Bad Block Errors? A Hands‑On Evaluation

This article presents a hands‑on evaluation of several large language models—including Mistral‑Small, Deepseek‑r1, Llama 3.3 and ChatGPT‑4‑go—on Oracle database bad‑block errors, RAG‑based document retrieval, and log‑driven reasoning, revealing performance gaps, scoring results, and practical DBA implications.

AIDatabaseLLM evaluation
0 likes · 11 min read
How Do LLMs Tackle Oracle Bad Block Errors? A Hands‑On Evaluation
Beijing SF i-TECH City Technology Team
Beijing SF i-TECH City Technology Team
Apr 7, 2025 · Artificial Intelligence

LLM Application in Text Information Detection and Extraction: A Case Study of Blue-Collar Recruitment Data Processing

This article explores the application of Large Language Models (LLM) in text information detection and extraction, focusing on blue-collar recruitment data processing. It details the implementation of LLM through prompt engineering, RAG enhancement, and model fine-tuning to improve data cleaning efficiency and accuracy.

AI applicationsLLMPrompt Engineering
0 likes · 31 min read
LLM Application in Text Information Detection and Extraction: A Case Study of Blue-Collar Recruitment Data Processing
DataFunSummit
DataFunSummit
Apr 7, 2025 · Artificial Intelligence

Bridging the Gap Between Large Models and Real‑World Applications with RAG and Agents

This article examines how Retrieval‑Augmented Generation (RAG) and multi‑agent technologies narrow the gap between large language models and practical deployment, highlighting their roles in operations automation, financial risk control, intelligent data governance, database localization, edge inference, and future AI‑driven solutions.

Data GovernanceLarge Language ModelsOperations Automation
0 likes · 8 min read
Bridging the Gap Between Large Models and Real‑World Applications with RAG and Agents
JD Cloud Developers
JD Cloud Developers
Apr 7, 2025 · Artificial Intelligence

Why Bigger Prompts Fail: Modular Strategies for Building Efficient AI Agents

This article explains why overloading prompts and tools harms AI‑Agent performance, and offers practical modular design, intent‑driven instruction splitting, and efficient context management strategies such as curated function‑call tools and dynamic RAG to reduce token costs, improve response speed, and avoid hallucinations.

AI AgentLLMModular Design
0 likes · 13 min read
Why Bigger Prompts Fail: Modular Strategies for Building Efficient AI Agents
Big Data Technology & Architecture
Big Data Technology & Architecture
Apr 3, 2025 · Artificial Intelligence

Understanding Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Vector Databases for LLM Integration

This article explains the Model Context Protocol (MCP) as a standard for LLM‑data integration, describes Retrieval‑Augmented Generation (RAG) techniques to reduce hallucinations, and introduces vector databases like Milvus that store high‑dimensional embeddings for efficient AI retrieval tasks.

LLMMCPMilvus
0 likes · 7 min read
Understanding Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Vector Databases for LLM Integration
DevOps
DevOps
Apr 2, 2025 · Artificial Intelligence

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

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

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

PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead

The PEAR framework introduces a position‑embedding‑agnostic attention re‑weighting method that detects and suppresses detrimental attention heads in large language models, dramatically improving retrieval‑augmented generation performance without adding any inference overhead, as demonstrated on multiple RAG benchmarks and LLM families.

Attention Re-weightingLLMPEAR
0 likes · 6 min read
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead
Tencent Cloud Developer
Tencent Cloud Developer
Apr 2, 2025 · Artificial Intelligence

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

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

AIHallucination MitigationKnowledge retrieval
0 likes · 9 min read
Understanding Retrieval‑Augmented Generation (RAG): Concepts, Types, and Development
Architect
Architect
Apr 1, 2025 · Artificial Intelligence

When to Fine‑Tune Large Language Models vs. Relying on Prompting and RAG

The article explains why most projects should start with prompt engineering or simple agent workflows, outlines the scenarios where model fine‑tuning adds real value, compares fine‑tuning with Retrieval‑Augmented Generation, and offers practical criteria for deciding which approach to adopt.

AI deploymentLarge Language ModelsLoRA
0 likes · 9 min read
When to Fine‑Tune Large Language Models vs. Relying on Prompting and RAG
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
Architect
Architect
Mar 29, 2025 · Artificial Intelligence

How Non‑AI Developers Can Build Powerful LLM Apps: Prompt Engineering, RAG, and AI Agents Explained

This article guides developers without an AI background through the fundamentals of building large‑language‑model applications, covering prompt engineering, multi‑turn interaction, function calling, retrieval‑augmented generation, vector databases, code assistants, and the MCP protocol for AI agents.

AI AgentEmbeddingFunction Calling
0 likes · 51 min read
How Non‑AI Developers Can Build Powerful LLM Apps: Prompt Engineering, RAG, and AI Agents Explained
Architect
Architect
Mar 26, 2025 · Artificial Intelligence

Agent Memory Mechanisms and Dify Knowledge Base Segmentation & Retrieval Details

This article explains the fundamentals of AI agent memory—including short‑term, long‑term, and working memory types and their storage designs—and then details Dify's knowledge‑base segmentation modes, indexing strategies, and retrieval configurations for effective RAG applications.

Agent MemoryDifyKnowledge Base
0 likes · 14 min read
Agent Memory Mechanisms and Dify Knowledge Base Segmentation & Retrieval Details
DaTaobao Tech
DaTaobao Tech
Mar 26, 2025 · Artificial Intelligence

Overview of Retrieval-Augmented Generation (RAG) and Related AI Technologies

The article surveys Retrieval‑Augmented Generation (RAG) as a solution to large language model limits—such as outdated knowledge, hallucinations, and security risks—by integrating vector‑database retrieval with LLM generation, and discusses related tools, multi‑agent frameworks, prompt engineering, fine‑tuning methods, and emerging optimization trends.

AI applicationsLLMPrompt Engineering
0 likes · 29 min read
Overview of Retrieval-Augmented Generation (RAG) and Related AI Technologies
Architect
Architect
Mar 22, 2025 · Artificial Intelligence

Understanding and Mitigating Failures in Retrieval‑Augmented Generation (RAG) Systems

Retrieval‑augmented generation (RAG) combines external knowledge retrieval with large language models to improve answer accuracy, but it often suffers from retrieval mismatches, algorithmic flaws, chunking issues, embedding biases, inefficiencies, generation errors, reasoning limits, formatting problems, system‑level failures, and high resource costs, which this article analyzes and offers solutions for.

AI reliabilityLLMRAG
0 likes · 32 min read
Understanding and Mitigating Failures in Retrieval‑Augmented Generation (RAG) Systems
Architect
Architect
Mar 19, 2025 · Artificial Intelligence

Choosing the Best Embedding Model for RAG: A Practical Guide Using MTEB Rankings

This guide explains how to leverage the Massive Text Embedding Benchmark (MTEB) to identify high‑performing embedding models for Retrieval‑Augmented Generation (RAG) and outlines key factors such as model size, dimension, language support, resource requirements, inference speed, domain suitability, long‑text handling, scalability, and cost.

AIEmbeddingMTEB
0 likes · 12 min read
Choosing the Best Embedding Model for RAG: A Practical Guide Using MTEB Rankings
Ops Development & AI Practice
Ops Development & AI Practice
Mar 19, 2025 · Artificial Intelligence

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

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

CAGCache AugmentationInference Optimization
0 likes · 11 min read
Can Cache‑Augmented Generation Outperform RAG? A Deep Dive into LLM Efficiency
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.

AIJavaRAG
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
Architect
Architect
Mar 15, 2025 · Artificial Intelligence

Why Building Your Own RAG System Is a Costly Mistake

The article explains that developing a custom Retrieval‑Augmented Generation (RAG) solution incurs hidden infrastructure, personnel, and security costs, leads to operational overload and budget overruns, and is rarely justified compared to purchasing a proven vendor solution.

AILLMRAG
0 likes · 11 min read
Why Building Your Own RAG System Is a Costly Mistake
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 14, 2025 · Artificial Intelligence

Solving Rate Limiting, Load Balancing, and Data Challenges in AI Inference with Tair

This article explains how AI inference services can tackle five core problems—rate limiting, load balancing, asynchronous processing, user data management, and index enhancement—by leveraging Tair's rich data structures, offering practical code examples, architectural diagrams, and a comparison with alternative solutions.

AI inferenceRAGRate Limiting
0 likes · 20 min read
Solving Rate Limiting, Load Balancing, and Data Challenges in AI Inference with Tair
DaTaobao Tech
DaTaobao Tech
Mar 14, 2025 · Artificial Intelligence

AI-Driven Engineering Efficiency: Practices and Insights from a Live-Streaming Team

The article recounts a live‑streaming team’s six‑month experiment using large‑language‑model AI to boost backend, frontend, testing, data‑science and data‑engineering productivity, detailing goals, LLM strengths and limits, and practical tactics such as task splitting, input refinement, human‑AI guidance, retrieval‑augmented generation and fine‑tuning, while emphasizing disciplined task design, prompt iteration, and future vertical integrations.

AIPrompt EngineeringRAG
0 likes · 17 min read
AI-Driven Engineering Efficiency: Practices and Insights from a Live-Streaming Team
Tencent Technical Engineering
Tencent Technical Engineering
Mar 10, 2025 · Artificial Intelligence

How Non‑AI Developers Can Build LLM Apps: Prompt Engineering, RAG, and Function Calling Explained

This guide shows non‑AI developers how to create large‑model applications by mastering prompt engineering, multi‑turn interactions, Retrieval‑Augmented Generation, function calling, and AI‑Agent integration, with practical code examples, tool design patterns, and deployment tips.

AI AgentEmbeddingFunction Calling
0 likes · 48 min read
How Non‑AI Developers Can Build LLM Apps: Prompt Engineering, RAG, and Function Calling Explained
DevOps
DevOps
Mar 9, 2025 · Artificial Intelligence

A Beginner's Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling, and AI Agents

This article provides a comprehensive introduction to developing large language model (LLM) applications, covering prompt engineering, zero‑ and few‑shot techniques, function calling, retrieval‑augmented generation (RAG) with embedding and vector databases, code assistants, and the MCP protocol for building AI agents, all aimed at non‑AI specialists.

AI AgentEmbeddingFunction Calling
0 likes · 48 min read
A Beginner's Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling, and AI Agents
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Mar 6, 2025 · Artificial Intelligence

Smart Q&A Knowledge Base Powered by Qwen2.5‑14B and Elasticsearch RAG

This article details a smart Q&A knowledge‑base system that integrates the Qwen2.5‑14B large language model with Elasticsearch vector search via RAG, covering data ingestion with FSCrawler, Chinese sentence embedding, Gradio UI, performance tests on a 483‑page book, architecture diagrams, code walkthroughs, and suggested enhancements.

Chinese EmbeddingElasticsearchFSCrawler
0 likes · 11 min read
Smart Q&A Knowledge Base Powered by Qwen2.5‑14B and Elasticsearch RAG
Cognitive Technology Team
Cognitive Technology Team
Mar 4, 2025 · Artificial Intelligence

Deep Searcher: An Open‑Source Agentic RAG Framework for Enterprise‑Level Search and Knowledge Retrieval

The article introduces Deep Searcher, an open‑source Agentic Retrieval‑Augmented Generation system that combines large language models, Milvus vector databases, and multi‑step reasoning to deliver enterprise‑grade search, reporting, and complex query capabilities, and compares its performance against traditional RAG and Graph RAG approaches.

LLMOpen SourceRAG
0 likes · 18 min read
Deep Searcher: An Open‑Source Agentic RAG Framework for Enterprise‑Level Search and Knowledge Retrieval
Tencent Cloud Developer
Tencent Cloud Developer
Mar 4, 2025 · Artificial Intelligence

A Practical Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling and AI Agents

The guide teaches non‑AI developers how to build practical LLM‑powered applications by mastering prompt engineering, function calling, retrieval‑augmented generation, and AI agents, and introduces the Modal Context Protocol for seamless tool integration, offering a clear learning path to leverage large language models without deep theory.

AI AgentFunction CallingLLM
0 likes · 48 min read
A Practical Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling and AI Agents
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 4, 2025 · Artificial Intelligence

Deploy a High‑Performance RAG Service with Hologres, DeepSeek, and PAI‑EAS

This guide walks you through building a Retrieval‑Augmented Generation (RAG) system by integrating Alibaba Cloud's Hologres vector store, the Proxima high‑performance vector engine, and DeepSeek large language models via PAI‑EAS, covering prerequisites, deployment steps, configuration, and inference verification.

AI deploymentDeepSeekHologres
0 likes · 12 min read
Deploy a High‑Performance RAG Service with Hologres, DeepSeek, and PAI‑EAS
AI Large Model Application Practice
AI Large Model Application Practice
Mar 3, 2025 · Artificial Intelligence

Can DeepSeek‑R1 Unlock True “Deep Thinking” for Enterprise RAG?

This article examines how swapping in DeepSeek‑R1 enhances Retrieval‑Augmented Generation with deeper reasoning, outlines its benefits and pitfalls—including slower inference, higher compute costs, and hallucinations—provides a simple hallucination test, and proposes an Agentic RAG research assistant to balance accuracy and creativity.

AI reasoningDeepSeekLLM
0 likes · 10 min read
Can DeepSeek‑R1 Unlock True “Deep Thinking” for Enterprise RAG?
Cognitive Technology Team
Cognitive Technology Team
Feb 28, 2025 · Artificial Intelligence

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

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

Artificial IntelligenceDeepSearcherGraph Database
0 likes · 20 min read
Comparative Study of Traditional RAG, GraphRAG, and DeepSearcher for Knowledge Retrieval and Generation
JavaEdge
JavaEdge
Feb 27, 2025 · Artificial Intelligence

How to Quickly Build a DeepSeek‑Powered Knowledge Base on Tencent Cloud

This guide walks through deploying the full‑feature DeepSeek V3+R1 model on Tencent Cloud, configuring a smart knowledge‑base application, importing documentation, enabling internet search, tuning retrieval parameters, and publishing the app for public use, all without writing code.

AIDeepSeekKnowledge Base
0 likes · 6 min read
How to Quickly Build a DeepSeek‑Powered Knowledge Base on Tencent Cloud
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Feb 25, 2025 · Artificial Intelligence

Build a RAG‑Powered Smart Q&A Assistant with Milvus, DeepSeek, and PAI LangStudio

This step‑by‑step guide shows how to assemble a Retrieval‑Augmented Generation (RAG) system using Alibaba Cloud Milvus vector search, the DeepSeek large language model, and PAI LangStudio, covering instance creation, data upload, model deployment, connection setup, flow design, and service invocation.

AI TutorialDeepSeekLLM
0 likes · 9 min read
Build a RAG‑Powered Smart Q&A Assistant with Milvus, DeepSeek, and PAI LangStudio
Ma Wei Says
Ma Wei Says
Feb 23, 2025 · Artificial Intelligence

How Microsoft’s PIKE‑RAG Builds Knowledge‑Driven AI Across Four Stages

The article explains Microsoft’s open‑source PIKE‑RAG system, detailing its four progressive stages—from knowledge‑base construction to creative multi‑agent reasoning—while describing the underlying modules, chunking strategies, multi‑granularity retrieval, and code snippets that enable specialized domain understanding and inference.

AI RetrievalKnowledge GraphLLM
0 likes · 11 min read
How Microsoft’s PIKE‑RAG Builds Knowledge‑Driven AI Across Four Stages
dbaplus Community
dbaplus Community
Feb 23, 2025 · Databases

Why Vector Databases Are Really Just Search Engines in Disguise

The article traces the evolution of embedding technology from a secret weapon of tech giants to a mainstream developer tool, explains the rapid rise and subsequent integration of vector databases into traditional search engines, and argues that vector databases are essentially search engines with added vector capabilities.

AI infrastructureEmbeddingsRAG
0 likes · 9 min read
Why Vector Databases Are Really Just Search Engines in Disguise
ZhongAn Tech Team
ZhongAn Tech Team
Feb 22, 2025 · Artificial Intelligence

How SkyReels, DeepSeek NSA, Grok‑3, and KG²RAG Are Shaping the Next AI Wave

This issue reviews China's first open‑source short‑film model SkyReels‑V1, DeepSeek's Native Sparse Attention breakthrough, xAI's massive Grok‑3 deployment on 200k H100 GPUs, and a knowledge‑graph‑guided RAG framework, highlighting their performance gains, architectural innovations, and industry impact.

AIKnowledge GraphLarge Models
0 likes · 15 min read
How SkyReels, DeepSeek NSA, Grok‑3, and KG²RAG Are Shaping the Next AI Wave
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 22, 2025 · Artificial Intelligence

Deploying DeepSeek Locally with Ollama, Building Personal and Organizational Knowledge Bases, and Integrating with Spring AI

This guide explains how to locally deploy the DeepSeek large‑language model using Ollama on Windows, macOS, and Linux, configure model storage and CORS, build personal and enterprise RAG knowledge bases with AnythingLLM and Open WebUI, and integrate the model into a Spring AI application via Docker and Docker‑Compose.

ContainerizationDeepSeekDocker
0 likes · 16 min read
Deploying DeepSeek Locally with Ollama, Building Personal and Organizational Knowledge Bases, and Integrating with Spring AI
Architecture and Beyond
Architecture and Beyond
Feb 22, 2025 · Artificial Intelligence

Understanding Retrieval‑Augmented Generation (RAG) and Its Role in Enhancing Large Language Models

The article explains how the inherent knowledge‑staleness, hallucination, lack of private data, non‑traceable output, limited long‑text handling, and data‑security concerns of large language models can be mitigated by Retrieval‑Augmented Generation, which combines external retrieval, augmentation, and generation to provide up‑to‑date, reliable, and secure AI responses.

AIKnowledge augmentationLLM
0 likes · 15 min read
Understanding Retrieval‑Augmented Generation (RAG) and Its Role in Enhancing Large Language Models
DataFunSummit
DataFunSummit
Feb 21, 2025 · Artificial Intelligence

Multimodal Retrieval‑Augmented Generation (RAG): Implementation Paths and Future Prospects

This article explores multimodal Retrieval‑Augmented Generation (RAG), detailing five core topics—including semantic extraction, visual‑language models, scaling strategies, technical roadmap choices, and a Q&A—while presenting three implementation pathways, performance evaluations, and future directions for AI‑driven document understanding.

RAGTensor Retrievaldocument understanding
0 likes · 11 min read
Multimodal Retrieval‑Augmented Generation (RAG): Implementation Paths and Future Prospects
Ma Wei Says
Ma Wei Says
Feb 21, 2025 · Artificial Intelligence

How PIKE‑RAG Boosts Retrieval‑Augmented Generation for Industrial AI

PIKE‑RAG, a Retrieval‑Augmented Generation framework from Microsoft Research, tackles knowledge source diversity, one‑size‑fits‑all limitations, and LLMs' lack of domain expertise by building multi‑layer heterogeneous graphs, task‑driven modular pipelines, and a staged L0‑L4 system for more accurate industrial AI responses.

AIKnowledgeGraphLLM
0 likes · 5 min read
How PIKE‑RAG Boosts Retrieval‑Augmented Generation for Industrial AI
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 20, 2025 · Artificial Intelligence

How LLMs Power Real-Time Interactive 3D Worlds in Unreal Engine

This article explains how large language models are integrated with Unreal Engine to enable natural‑language‑driven 3D model search, manipulation, and scene understanding, detailing metadata extraction, vision‑language labeling, RAG‑based retrieval, and function‑call translation for interactive virtual environments.

3D interactionLLMRAG
0 likes · 21 min read
How LLMs Power Real-Time Interactive 3D Worlds in Unreal Engine
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Feb 19, 2025 · Artificial Intelligence

Build a DeepSeek AI Assistant with PAI‑RAG: Internet Search & Enterprise Knowledge Base

This guide walks you through using Alibaba Cloud's PAI‑RAG platform to deploy a DeepSeek large‑language‑model assistant that combines real‑time web search with an enterprise knowledge‑base, covering deployment, network‑search configuration, testing, and advanced enterprise features.

AI AssistantDeepSeekEnterprise Knowledge Base
0 likes · 10 min read
Build a DeepSeek AI Assistant with PAI‑RAG: Internet Search & Enterprise Knowledge Base
JD Retail Technology
JD Retail Technology
Feb 18, 2025 · Artificial Intelligence

Engineering Practices of JD Advertising Agent: JDZunTong Intelligent Assistant

JD’s advertising R&D team created the JDZunTong Intelligent Assistant by engineering a modular Agent platform that combines advanced Retrieval‑Augmented Generation (RAG 1.0 → 2.0) and Function‑Call capabilities, a visual designer, custom tool registration, and a native Python workflow engine to deliver intelligent customer service, data queries, and ad creation for merchants.

AIAgentJD Advertising
0 likes · 18 min read
Engineering Practices of JD Advertising Agent: JDZunTong Intelligent Assistant
macrozheng
macrozheng
Feb 17, 2025 · Artificial Intelligence

Unlock DeepSeek4j 1.4: Build a Private AI Knowledge Base with Spring Boot

This guide explains why DeepSeek4j is needed, its core features, and provides step‑by‑step instructions—including dependency setup, configuration, code examples, and a complete RAG pipeline using Milvus—to help developers quickly create a private AI knowledge base with Spring Boot.

AIDeepSeek4jMilvus
0 likes · 12 min read
Unlock DeepSeek4j 1.4: Build a Private AI Knowledge Base with Spring Boot
Liangxu Linux
Liangxu Linux
Feb 16, 2025 · Artificial Intelligence

Build a Free Private AI with DeepSeek, Ollama, and Local Knowledge Base

This guide explains how to locally deploy the open‑source DeepSeek model using Ollama, enhance interaction with Chatbox and Page Assist, and connect a local knowledge base via AnythingLLM's RAG architecture, providing step‑by‑step instructions, hardware requirements, and API examples for a self‑hosted AI system.

AI deploymentAnythingLLMDeepSeek
0 likes · 22 min read
Build a Free Private AI with DeepSeek, Ollama, and Local Knowledge Base
AIWalker
AIWalker
Feb 14, 2025 · Artificial Intelligence

ImageRAG: Leveraging RAG and AIGC to Elevate Image Generation Quality

ImageRAG introduces a dynamic retrieval‑augmented generation framework that integrates visual language models and CLIP‑based similarity search to supply reference images, enabling diffusion models like OmniGen and SDXL to better render rare and fine‑grained concepts, as demonstrated through extensive quantitative and qualitative experiments.

AIGCImageRAGOmniGen
0 likes · 18 min read
ImageRAG: Leveraging RAG and AIGC to Elevate Image Generation Quality
DataFunSummit
DataFunSummit
Feb 14, 2025 · Artificial Intelligence

Building Large‑Scale Recommendation Systems with Big Data and Large Language Models on Alibaba Cloud AI Platform

This presentation details how Alibaba Cloud's AI platform integrates big‑data pipelines, feature‑store services, and large language model capabilities to construct high‑performance search‑recommendation architectures, covering system design, training and inference optimizations, LLM‑driven use cases, and open‑source RAG tooling.

AI PlatformBig DataFeature Store
0 likes · 17 min read
Building Large‑Scale Recommendation Systems with Big Data and Large Language Models on Alibaba Cloud AI Platform
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Feb 14, 2025 · Artificial Intelligence

Deploy a DeepSeek AI App with Web Search & Private Knowledge Base in 30 Minutes

This guide walks you through deploying DeepSeek models on Alibaba Cloud PAI, integrating SerpAPI for live web search, building a private knowledge base, and assembling a RAG-enabled chatbot workflow, all within 30 minutes, enabling enterprises to create intelligent applications that combine large‑model capabilities with up‑to‑date information.

AI ApplicationAlibaba CloudDeepSeek
0 likes · 7 min read
Deploy a DeepSeek AI App with Web Search & Private Knowledge Base in 30 Minutes
AI Algorithm Path
AI Algorithm Path
Feb 13, 2025 · Artificial Intelligence

How to Build a Local RAG Knowledge Base with DeepSeek‑R1 and Ollama

This article walks through setting up a local Retrieval‑Augmented Generation (RAG) system using the open‑source DeepSeek‑R1 model run via Ollama, covering installation, model selection, PDF ingestion with LangChain, semantic chunking, FAISS vector store creation, RetrievalQA chain construction, and a Streamlit UI for querying.

DeepSeek-R1FAISSLangChain
0 likes · 8 min read
How to Build a Local RAG Knowledge Base with DeepSeek‑R1 and Ollama
Java Architecture Diary
Java Architecture Diary
Feb 13, 2025 · Artificial Intelligence

Create a Java RAG System Using DeepSeek R1, Milvus, and Spring

This guide walks through building a Java RAG system with DeepSeek R1, Milvus, and Spring, covering environment setup, vector model integration via OpenAI protocol, Maven dependencies, data embedding, and a chat endpoint that combines semantic retrieval with LLM generation.

AI integrationDeepSeekMilvus
0 likes · 11 min read
Create a Java RAG System Using DeepSeek R1, Milvus, and Spring
DevOps
DevOps
Feb 12, 2025 · Artificial Intelligence

A Comprehensive Guide to Prompt Engineering, RAG, and Optimization Techniques for Large Language Models

This article presents a systematic framework for crafting effective prompts, detailing the universal prompt template, role definition, task decomposition, RAG integration, few‑shot examples, memory handling, and parameter tuning to enhance large language model performance across diverse applications.

Prompt EngineeringPrompt TemplatesRAG
0 likes · 24 min read
A Comprehensive Guide to Prompt Engineering, RAG, and Optimization Techniques for Large Language Models
Architect
Architect
Feb 12, 2025 · Artificial Intelligence

Master Prompt Engineering: A Universal Framework for LLMs

This article presents a comprehensive, step‑by‑step Prompt engineering framework—including role definition, problem description, goal setting, and requirement specification—augmented with techniques such as RAG, few‑shot examples, memory handling, and parameter tuning, enabling users to craft effective prompts for large language models across domains.

AI Prompt OptimizationFew-shotLarge Language Models
0 likes · 27 min read
Master Prompt Engineering: A Universal Framework for LLMs
Alibaba Cloud Native
Alibaba Cloud Native
Feb 12, 2025 · Artificial Intelligence

Boost AI Agents with Spring AI Alibaba: 20+ RAG Sources & Tool‑Calling Integrations

This article explains how Spring AI Alibaba enables AI agents to leverage Retrieval‑Augmented Generation and Tool Calling by providing over twenty ready‑made RAG data source connectors and more than twenty function‑calling interfaces, along with practical code examples for integrating document readers and weather services.

Document ReaderFunction CallingJava
0 likes · 12 min read
Boost AI Agents with Spring AI Alibaba: 20+ RAG Sources & Tool‑Calling Integrations
DataFunTalk
DataFunTalk
Feb 11, 2025 · Artificial Intelligence

Roundtable on Enhancing Large Model Effectiveness: RAG, Tool Use, and Knowledge Engineering

Experts from Dipu, Ant Financial, iKang, and Zhihu discuss practical strategies for improving large model performance, covering RAG, tool‑using, offline knowledge engineering, multimodal training, evaluation metrics, and future trends, while sharing case studies from manufacturing, healthcare, retail, and C‑end applications.

Knowledge EngineeringLarge Language ModelsRAG
0 likes · 9 min read
Roundtable on Enhancing Large Model Effectiveness: RAG, Tool Use, and Knowledge Engineering
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Feb 10, 2025 · Artificial Intelligence

Eight Ways Enterprises Can Leverage DeepSeek

The article outlines eight distinct enterprise strategies for adopting DeepSeek, categorizing them by model maturity, available data types, and specific business challenges, and maps these approaches onto four capability tiers—from basic compliance requirements to advanced multimodal, low‑cost solutions.

AI agentsDeepSeekEnterprise AI
0 likes · 3 min read
Eight Ways Enterprises Can Leverage DeepSeek
iKang Technology Team
iKang Technology Team
Feb 7, 2025 · Artificial Intelligence

Retrieval‑Augmented Generation (RAG) with LangChain: Concepts and Python Implementation

Retrieval‑Augmented Generation (RAG) using LangChain lets developers enhance large language models by embedding user queries, fetching relevant documents from a vector store, inserting the context into a prompt template, and generating concise, source‑grounded answers, offering low‑cost, up‑to‑date knowledge while reducing hallucinations and fine‑tuning expenses.

LLMLangChainRAG
0 likes · 10 min read
Retrieval‑Augmented Generation (RAG) with LangChain: Concepts and Python Implementation
Cognitive Technology Team
Cognitive Technology Team
Feb 6, 2025 · Artificial Intelligence

DeepSeek Model Guide: 10 Practical Tips and Usage Techniques

This article presents ten detailed techniques for effectively using DeepSeek's large language models—including mode selection, model comparisons, knowledge updates, prompt engineering, RAG, file uploads, API access, and open‑source resources—while offering concrete examples and code snippets for each feature.

AI APIDeepSeekLarge Language Model
0 likes · 12 min read
DeepSeek Model Guide: 10 Practical Tips and Usage Techniques
DataFunSummit
DataFunSummit
Jan 30, 2025 · Databases

Mature Practices for Building Risk‑Control Knowledge Graphs on NebulaGraph and Leveraging Large Language Models

This article explains how NebulaGraph’s large‑scale graph database can be used to construct real‑time risk‑control knowledge graphs, describes practical applications such as community detection and path analysis, and explores how large language models enhance graph queries through Text‑to‑GQL, agents, exploration chains, and semi‑structured knowledge extraction.

AIGraph DatabaseKnowledge Graph
0 likes · 11 min read
Mature Practices for Building Risk‑Control Knowledge Graphs on NebulaGraph and Leveraging Large Language Models
Architect
Architect
Jan 27, 2025 · Artificial Intelligence

How to Build a Retrieval‑Augmented Generation QA Assistant for an Open Platform

This article details a step‑by‑step design of a RAG‑based intelligent Q&A assistant for the DeWu Open Platform, covering background, RAG fundamentals, system architecture, technology selection, prompt engineering with CO‑STAR, data preprocessing, vector store setup, LangChain.js implementation, similarity search, runnable chaining, debugging, and future prospects.

AILLMLangChain
0 likes · 28 min read
How to Build a Retrieval‑Augmented Generation QA Assistant for an Open Platform
DataFunSummit
DataFunSummit
Jan 26, 2025 · Artificial Intelligence

ChatBI in Automotive Enterprises: Challenges, Architecture, and Implementation

This article examines the rise of ChatBI in automotive companies, outlining current BI challenges, the five “no” and five “difficulties” issues, the motivations for adopting ChatBI, its evolving architecture, and practical implementation steps to achieve data‑driven decision making.

AIAutomotiveChatBI
0 likes · 17 min read
ChatBI in Automotive Enterprises: Challenges, Architecture, and Implementation
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
Baidu Geek Talk
Baidu Geek Talk
Jan 20, 2025 · Industry Insights

How Baidu’s Qianfan AppBuilder Is Redefining AI‑Native App Development

The interview explores how Baidu Cloud's Qianfan AppBuilder platform evolves from traditional coding to AI‑native low‑code development, detailing the impact of large‑model agents, Retrieval‑Augmented Generation, security, multimodal support, and future roadmap on enterprise productivity and digital transformation.

AI agentsAI native appsEnterprise AI
0 likes · 18 min read
How Baidu’s Qianfan AppBuilder Is Redefining AI‑Native App Development
Zhihu Tech Column
Zhihu Tech Column
Jan 17, 2025 · Artificial Intelligence

Zhihu Direct Answer: Product Overview and Technical Practices

This article summarizes the key technical insights from Zhihu Direct Answer, an AI-powered search product, covering its product overview, RAG framework, query understanding, retrieval strategies, chunking, reranking, generation techniques, evaluation methods, and engineering optimizations for cost and performance.

AI SearchChunkingEngineering Optimization
0 likes · 13 min read
Zhihu Direct Answer: Product Overview and Technical Practices
Architecture Digest
Architecture Digest
Jan 16, 2025 · Artificial Intelligence

Redis Introduces Multi‑Threaded Query Engine to Boost Vector Search Performance for Generative AI

Redis has unveiled a multi‑threaded query engine that dramatically increases query throughput and lowers latency for vector similarity searches, offering up to 16× performance gains and enabling real‑time Retrieval‑Augmented Generation (RAG) workloads in generative AI applications.

Database PerformanceGenerative AIRAG
0 likes · 7 min read
Redis Introduces Multi‑Threaded Query Engine to Boost Vector Search Performance for Generative AI
DaTaobao Tech
DaTaobao Tech
Jan 15, 2025 · Mobile Development

How AI Transformed Taobao’s Post‑Purchase Info‑Flow Across Android, iOS, and Weex

Facing the challenge of maintaining four codebases for Taobao’s post‑purchase information flow, the team leveraged AI‑driven code generation, prompt engineering, and RAG to automate template conversion from DX to Weex, dramatically cutting development cycles, reducing manual effort, and improving monitoring and stability across Android, iOS, and HarmonyOS.

AICross‑platform developmentMobile Engineering
0 likes · 20 min read
How AI Transformed Taobao’s Post‑Purchase Info‑Flow Across Android, iOS, and Weex