This article explains what vector stores are, outlines their core workflow of adding, querying, and searching embeddings, compares popular back‑ends like FAISS, Chroma, and Pinecone, and walks through a complete Chinese‑language example using LangChain’s FAISS integration with detailed code and result analysis.
This article details the implementation of an LLM‑based code‑review agent in a C3‑level secure repository, describing its RAG‑enhanced knowledge base, CI pipeline integration, real‑world results, prompt engineering, and ongoing optimization to boost review efficiency and defect detection.
This guide explains why large documents must be broken into semantic chunks for LLMs, introduces core parameters like chunk_size and chunk_overlap, compares LangChain's various splitters, and walks through a complete Python example that loads a long text, configures a RecursiveCharacterTextSplitter, and inspects the resulting chunks.
This presentation by iQIYI’s Technical Director Zhang Xiaoming details the evolution of BI systems, introduces the Chat BI framework, explains its three‑step implementation, outlines architectural design, data‑warehouse integration, performance optimizations, and user‑operation strategies, revealing how AI and RAG empower smarter data analytics.
The Higress 2.0 release introduces 30 changes—13 new features such as a RAG MCP server and ECDS‑based configuration refactor, 7 bug fixes, 5 refactorings, documentation updates and a test improvement—providing developers with enhanced knowledge‑management capabilities, more stable routing, and clearer documentation for cloud‑native service‑mesh environments.
Meta’s new REFRAG framework lets a lightweight encoder compress retrieved text into semantic tags, enabling large language models to answer queries with far fewer tokens, lower latency, and higher throughput, while preserving core meaning and allowing flexible placement of compressed information within prompts.
This comprehensive AI agent development course covers 18 chapters, ranging from fundamental concepts and architecture to large‑model integration, tool and browser control, memory, RAG self‑learning, sandboxing, database manipulation, multi‑agent architectures, code assistance, and a real‑world frontend automation project, complete with source code and documentation.
This article explains how to design a high‑performing AI Agent by constructing a robust knowledge base, orchestrating efficient workflows, and crafting precise prompts, covering vector storage, graph databases, retrieval strategies, and practical prompt‑engineering techniques.
This article explains how veDB-Search provides a one‑stop hybrid search service that combines vector, full‑text, and scalar queries using only SQL, reducing development complexity and operational overhead for AI agents and large‑scale AI applications.
This article examines Tencent's large language model deployments across diverse business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, while explaining the underlying technologies of Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agent systems.
This article explores Tencent’s large language model deployments across diverse business scenarios—content generation, intelligent customer service, and role‑playing—detailing the underlying RAG, GraphRAG, and Agent technologies, their principles, practical implementations, and the advantages they bring to enterprise AI solutions.
This review examines how formal knowledge representations (ontologies) can be integrated with large language models to enhance reasoning, reduce hallucinations, and improve factual reliability, outlining three roles—information provider, reasoner, validator—while analyzing recent frameworks, open‑source projects, and future research challenges.
This article explains Retrieval‑Augmented Generation (RAG), an AI framework that combines external knowledge retrieval with large language models, covering its motivations, data preparation, chunking strategies, vectorization, storage, query processing, retrieval, reranking, prompt engineering, and LLM generation, plus practical optimization tips.
This article examines the challenges of processing massive multimodal data in enterprises and presents a knowledge‑augmentation framework that leverages Retrieval‑Augmented Generation, memory‑inspired architecture, and feedback loops to enable reliable, scalable AI‑driven decision making across diverse business scenarios.
This guide breaks down the complex terminology of large language models—explaining tokens, transformers, self‑attention, RAG, scaling laws, dense vs. sparse architectures, and training stages—using clear analogies and step‑by‑step explanations so readers can confidently understand and work with modern AI systems.
This article explains why initial retrieval in Retrieval‑Augmented Generation often yields noisy results, describes how rerankers act as quality filters to improve relevance, compares the leading 2025 reranker models—including Cohere, bge‑reranker, Voyage, Jina, FlashRank, and MixedBread—and provides code snippets, evaluation metrics, and guidance for selecting the right model for specific use cases.
This article explains Retrieval‑Augmented Generation (RAG), an AI framework that combines traditional information retrieval with large language models, detailing its core workflow—from knowledge preparation, chunking, and embedding to vector database storage and the question‑answering stage—while highlighting key challenges, tools, and optimization strategies.
This article explains Retrieval‑Augmented Generation (RAG), an AI framework that combines traditional information retrieval with large language models, covering its core workflow—from knowledge preparation, data cleaning, and metadata extraction to query preprocessing, vector retrieval, reranking, information integration, and final LLM generation, while also reviewing common embedding models and vector databases.
This article walks through MindsDB’s Knowledge Base feature, showing how to map data sources, create vector indexes, perform semantic search, combine multiple sources with SQL joins, automate updates via jobs, and construct powerful RAG pipelines and Data Agents for AI‑driven query answering.
This article details how Tencent's advertising R&D team tackled lengthy, experience‑driven test case creation by deploying AIGC‑powered demand analysis, Prompt + RAG knowledge retrieval, and multi‑stage automated validation, ultimately boosting test case adoption from under 20% to nearly 60% while reducing manual effort and iteration time.
This article explains how Retrieval‑Augmented Generation (RAG) combines large language models with vector databases to let non‑technical staff query massive membership data using plain language, detailing the workflow, technical architecture, optimization challenges, and real‑world impact on data‑driven decision making.
This repository offers a comprehensive, step‑by‑step LangChain tutorial series that walks developers through installation, the LangChain Expression Language, streaming, parallel execution, callbacks, serialization, model customization, prompt templates, memory, multimodal support, and advanced tools like LangGraph and LangSmith, enabling the creation of sophisticated AI applications.
This article explores the problem of large‑model hallucinations, explains its definitions and classifications, analyzes root causes in data, algorithms and inference, and presents detection methods and practical mitigation techniques such as RAG, decoding strategies, and model‑enhancement approaches, illustrated with real‑world 360 use cases and future research directions.
This article guides front‑end developers through the evolution of AI agents—from early chatbots to modern multimodal agents—covering LLM fundamentals, prompt engineering, LangChain.js workflow creation, Retrieval‑Augmented Generation, model context protocols, and future multi‑agent technologies.
This article reviews the FinKario paper, which introduces an event‑augmented financial knowledge graph and a two‑stage RAG retrieval strategy that together enable real‑time knowledge updates and efficient integration of long‑form research reports, yielding a Sharpe ratio of 4.9 and outperforming baseline LLMs and institutional strategies in back‑testing.
This article examines Tencent's large language model deployments across content generation, intelligent customer service, and role‑playing, detailing the underlying SFT, Retrieval‑Augmented Generation, GraphRAG, and Agent technologies that enable smarter, more reliable AI solutions.
This article examines Tencent's large language model deployments across diverse business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑play, and explains the underlying technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and intelligent agents—that enable these applications.
This guide explains why RAG systems often produce off‑topic answers, outlines how to measure hit‑rate, retrieval, reranking and generation metrics, provides step‑by‑step evaluation pipelines, code examples, real‑world case studies, and interview‑ready templates for diagnosing and optimizing each stage of the pipeline.
This tutorial walks through building a Retrieval‑Augmented Generation (RAG) system from the ground up, covering document parsing, text chunking strategies, vector store creation with ChromaDB, semantic search, prompt engineering for LLMs, conversation memory, coreference handling, and practical optimization tips, all illustrated with complete Python code.
This article details how Zhuanzhuan leverages large language models to enhance on‑site device inspection through a three‑stage pipeline—intent understanding, retrieval‑augmented generation QA, and automated knowledge‑base upkeep—highlighting technical innovations, workflow integration, and the resulting operational benefits.
This article explores Tencent’s large language model across diverse business scenarios—content generation, intelligent customer service, role‑playing, and more—detailing the principles and practical uses of Retrieval‑Augmented Generation (RAG), GraphRAG, and Agent technologies, and how they enhance model intelligence and user experience.
The Zhiyu model, a 32‑billion‑parameter SRE‑focused LLM, combines extensive domain knowledge, enhanced professional skills, and deterministic RAG to deliver precise, actionable insights for intelligent operations, backed by a robust multi‑source training pipeline, staged training, and flexible deployment options.
Rewriting user prompts before vector similarity retrieval enhances semantic clarity, aligns language with document style, eliminates ambiguity, and guides the embedding model to focus on relevant information, thereby improving retrieval effectiveness.
In an interview, Di Xingxing of Autohome details their AI+Data framework—unified lake‑warehouse, intelligent engine, and agent services—that breaks data silos, blends traditional models with LLMs, leverages causal inference and RAG knowledge bases, and uses continuous feedback to build explainable, evolving data agents for accurate sales forecasting, competitive analysis, and end‑to‑end business automation in the automotive industry.
Agentic AI transforms static large language models into autonomous agents by adding perception, goal orientation, planning, action, interaction, and iterative loops, tracing its evolution from early chatbots through Prompt Engineering, ReAct, AutoGPT, OpenAI Function Calling, to modern multi‑agent frameworks, while addressing challenges like memory, hallucinations, and scalability.
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.
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.
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.
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.
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.
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.
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.
This article outlines the fundamental RAG workflow—from data indexing and querying to advanced modular designs—highlights key challenges such as retrieval accuracy, model robustness, context limits, and performance, and traces the evolution from naive to modular RAG systems.
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.
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.
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.
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.
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%.
RAGFlow is an open‑source Retrieval‑Augmented Generation engine that lets large language models query diverse internal documents, provides source citations, supports many file formats, and can be quickly deployed via Docker following a step‑by‑step guide.
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.
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.
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.
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.
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.
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.
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.
This article examines the limitations of large language models' internal knowledge, explains how retrieval‑augmented generation (RAG) and tool‑augmented generation address these limits, and traces the evolution from simple retrieve‑then‑generate pipelines to autonomous, multi‑modal AI agents.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
This article details how Zhaozhuan transformed its customer‑service robot from relying on external NLP providers to a fully self‑developed AI solution, covering technical choices, model training, RAG integration, deployment strategies, and future directions.
The article details how a legal team tackled inefficient labor law document search by creating a RAG‑based intelligent Q&A assistant with Claude and the open‑source Coco AI toolkit, covering solution comparison, environment setup, data preparation, testing, and optimization insights.
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.
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.
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.
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.
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.
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.
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.
This tutorial explains how to use Milvus 2.5's new full‑text, BM25 keyword matching, and hybrid search capabilities—including step‑by‑step setup, schema design, code examples, and RAG integration—to achieve high recall and precision in large‑scale AI vector retrieval scenarios.
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.
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.
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.
This guide explains how to combine Alibaba Cloud Milvus, a high‑performance vector database, with the low‑code AI platform Dify to create an enterprise‑level Retrieval‑Augmented Generation (RAG) application, covering architecture, installation, data ingestion, and verification steps.
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.
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.
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.
This article explains how to create a RAG‑based intelligent QA system that fetches the latest documentation (e.g., PlantUML) before querying Gemini, detailing knowledge‑base creation, embedding, vector store management, LangChain integration, and deployment tips.
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.
This article walks through retrieving ten years of Youdao Cloud Notes, selecting a RAG implementation (self‑built or using Coco AI), configuring cookies, loading the notes locally, and integrating a large language model to enable full‑text search and intelligent question‑answering.
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.
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.
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.
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.
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.
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.
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.
