Amazon Nova Web Grounding is an out‑of‑the‑box RAG tool for Amazon Bedrock that automatically retrieves and cites up‑to‑date information, reduces hallucinations, and lets developers create accurate, real‑time AI applications using simple Python code.
This article explores how the explosion of unstructured data exposes the limits of traditional OCR and shows how emerging multimodal large language models provide end‑to‑end document understanding, reduce pipeline complexity, cut training costs, enable hybrid retrieval‑augmented generation, and drive real‑world industry deployments.
This article explains the limitations of basic Retrieval‑Augmented Generation (RAG), introduces Rerank technology as a two‑step refinement process, compares dual‑encoder and cross‑encoder methods, and reviews popular Rerank models to help developers build more precise AI‑driven retrieval systems.
This article details a backend developer's two‑year AI journey, the challenges of rapid model advances, and how applying microservice principles to Retrieval‑Augmented Generation (RAG) creates a scalable, multi‑agent platform for insurance knowledge, memory, and intelligent agents.
This article outlines practical techniques—including prompt engineering, domain fine‑tuning, retrieval‑augmented generation, structured outputs, workflow constraints, model parameter control, behavior rules, risk‑based AI participation, and comprehensive governance—to curb the unpredictability of large language model agents in enterprise settings.
This talk examines the core challenges facing AI coding agents—limited context windows, knowledge accumulation, and software‑engineering complexity—while outlining practical solutions such as context providing, RAG, fine‑tuning, online learning, feedback loops, and multi‑agent collaboration to move toward truly human‑like, continuously learning coding assistants.
The Postal Savings Bank of China built an AI‑driven, engineering‑plus‑agent framework that evaluates large models, decouples generation from formatting, integrates a knowledge‑base with RAG, and delivers a pilot‑tested service that boosts test case adoption rates by up to 3.7 times while cutting manual effort to hours.
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 explains how RAGFlow upgrades a traditional RAG system from a passive question‑answer engine to an active problem‑solving agent by integrating the ReAct reasoning‑action‑observation loop, a visual canvas workflow, and a modular component‑tool ecosystem, with concrete Python implementations and code examples.
This article provides a comprehensive technical overview of RAGFlow, an industrial‑grade Retrieval‑Augmented Generation platform, detailing its architecture, core components such as DeepDoc, intelligent chunking, embedding integration, multi‑stage retrieval, and agent workflow, while comparing it with traditional RAG shortcomings.
This article provides a detailed technical walkthrough of RAGFlow's architecture, covering its microservice design, directory layout, layered structure, cloud‑native deployment, core modules such as DeepDoc, RAG engine, Agent system, and web UI, as well as multi‑tenant isolation, streaming responses, asynchronous task handling, concurrency controls, scalability strategies, and a complete request‑lifecycle example for document upload.
The article introduces CID-GraphRAG, a novel framework that combines intent‑driven graphs with semantic similarity search to improve multi‑turn intelligent customer service, detailing its architecture, dual‑layer retrieval mechanism, evaluation against baseline models, and future research directions.
Amid the rapid rise of generative AI, this article examines the limitations of large language models and explains how Retrieval‑Augmented Generation (RAG), followed by the advanced paradigms DeepSearch and DeepResearch, progressively enhance knowledge handling through dynamic retrieval, multi‑agent reasoning, and autonomous research capabilities.
This article examines Tencent's large language model deployments across diverse business scenarios, detailing how Retrieval‑Augmented Generation, GraphRAG, and autonomous agents boost model intelligence, improve user experience, and enable advanced content generation, understanding, and multi‑step reasoning.
Integrating structured knowledge graphs with generative AI provides traceable, explainable, and high‑precision reasoning across domains such as medicine, finance, and law, through techniques like Retrieval‑Augmented Generation, graph neural networks, and adaptive planning, dramatically reducing hallucinations and boosting expert‑level performance.
This article explores the three stages of AI knowledge representation—vector embeddings, graph‑based structures, and the emerging hybrid approach that combines vectors, graphs, and large language models—to illustrate how modern Retrieval‑Augmented Generation systems achieve both semantic similarity and precise relational reasoning.
This article surveys over 1,400 papers to define context engineering as a systematic discipline that structures retrieval, memory, tools, and multi‑agent coordination for LLMs, highlighting the critical asymmetry between understanding long contexts and generating equally complex outputs.
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 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.
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 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.
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.
Large language models often generate confident but false statements—a phenomenon called hallucination—because they predict the next token based on statistical patterns rather than factual understanding, and this article explains the underlying mechanisms and practical mitigation strategies.
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 article describes a QCon talk that combines causal inference with large language models to build a retrieval‑augmented generation pricing system for e‑commerce, detailing the three‑step algorithm, LLM‑driven modeling challenges, process‑reward tree search, reinforcement‑learning fine‑tuning, and experimental gains in accuracy and speed.
This article reviews a series of recent research papers on large‑model agents, covering topics such as reinforcement‑learning‑driven ML agents, premise‑critique ability of LLMs, long‑term tool‑augmented LLM evaluation, agentic RAG, set‑based retrieval for multi‑hop QA, mobile VLM agents, and broader surveys of LLM applications, summarizing each work’s problem statement, prior approaches, novel contributions, experimental results, limitations, and future directions.
This article traces the evolution from prompt engineering to context engineering, detailing technical milestones, core concepts, practical strategies, and future trends that together reshape large language model applications and enable sophisticated AI agents across diverse domains.
An in‑depth guide outlines the nine core technologies—ranging from autonomous agent fundamentals and multi‑agent collaboration to workflow orchestration, retrieval‑augmented generation, fine‑tuning, function calling, model context protocols, agent‑to‑agent communication, and AI‑driven UI—required to design, deploy, and scale enterprise‑grade AI agents.
This article explains how GraphRAG combines knowledge graphs with retrieval‑augmented generation to overcome the limitations of vector‑only RAG, delivering higher accuracy, better explainability, easier development, and stronger governance for generative AI applications across various domains.
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.
This article walks through the complete GraphRAG workflow—from environment setup and indexing to command‑line and Python API queries—demonstrating how to build a knowledge graph, tune prompts, and retrieve high‑quality answers using Microsoft’s GraphRAG implementation.
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.
FilMaster is a pioneering AI system that learns cinematic principles from a 440,000‑shot movie database, combines multimodal LLMs, RAG, and audience‑centric rhythm control to generate editable, high‑quality films, and outperforms prior methods by over 50% on the new FilmEval benchmark.
This guide breaks down the ten core building blocks of a production‑ready RAG pipeline—from input handling and vector stores to prompt engineering, LLM inference, observability, and evaluation—showing why each piece matters, common pitfalls, and practical best‑practice recommendations.
The article enumerates common pitfalls of Retrieval‑Augmented Generation—such as missing content, low‑rank document misses, context limits, format errors, incomplete answers, scalability bottlenecks, complex PDF extraction, data‑quality issues, domain adaptation gaps, hallucinations, and feedback‑loop deficiencies—and offers concrete mitigation strategies ranging from data cleaning and prompt design to hybrid search, hierarchical retrieval, document compression, and automated evaluation.
This article introduces Retrieval‑Augmented Generation (RAG), explains its core components—knowledge embedding, retriever, and generator—covers practical system construction, optimization techniques, evaluation metrics, and advanced paradigms such as GraphRAG and Multi‑Modal RAG, while highlighting a comprehensive guidebook for hands‑on implementation.
This article explains how DeepSeek's large‑model‑driven search system overcomes traditional keyword‑matching limits, improves long‑tail query coverage, and delivers personalized, accurate government service results through intent parsing, knowledge‑graph retrieval, and generative optimization.
This article systematically explains the concepts of Deep Search and Deep Research, contrasts them with traditional Retrieval‑Augmented Generation, reviews leading commercial and open‑source solutions, details their architecture for code retrieval, and outlines future plans for specialized code‑search agents.
The article explains Retrieval‑Augmented Generation (RAG) by describing how a programmer, frustrated with oversized prompts for a large language model, discovers that retrieving relevant document fragments, embedding them, and feeding the augmented context to the model yields accurate, fact‑based answers.
This article explains why Retrieval‑Augmented Generation (RAG) is essential for AI‑driven customer service, outlines the limitations of large language models, details the three‑stage RAG workflow (indexing, retrieval, generation), and shows how to implement it with FastGPT, vector databases, and LangChain.
The article reviews the RAG-MCP framework, which combines Retrieval‑Augmented Generation with Model Context Protocol to reduce prompt bloat and improve tool‑selection accuracy for large language models by first retrieving the most relevant tools before feeding them to the LLM.
Agentic RAG‑R1, an open‑source project from Peking University, combines Retrieval‑Augmented Generation with an agentic AI loop, introduces the GRPO reinforcement‑learning optimizer, supports LoRA‑based fine‑tuning, quantization and multimodal tool calls, and demonstrates significant accuracy gains on the MedQA benchmark across both Chinese and English test sets.
This article explores how knowledge graphs can be tightly integrated with large language models through prompt engineering, fine‑tuning, retrieval‑augmented generation, reasoning collaboration, and knowledge agents, outlining technical pathways, practical implementations, and future research directions across AI domains.
This guide walks you through the fundamentals of Retrieval‑Augmented Generation, introduces the open‑source RAGFlow framework, details installation steps, shows how to integrate DeepSeek LLMs, and explores practical application scenarios such as intelligent customer service and enterprise document QA.
This tutorial explains how to overcome the limitations of Retrieval‑Augmented Generation by using the Model Context Protocol (MCP) together with a MongoDB database, providing detailed setup steps, configuration examples, and performance comparisons that demonstrate significantly higher query precision for large language models.
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.
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.
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.
This tutorial introduces the fundamentals of LangChain, explains large language models, prompt engineering, word embeddings, and demonstrates how to use the Java implementation LangChain4j with Maven dependencies, model I/O, memory, retrieval, chains, and agents to build sophisticated LLM‑driven applications.
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.
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.
ImageRAG introduces a retrieval‑augmented generation framework that dynamically fetches relevant images to guide diffusion models, dramatically improving the synthesis of rare and fine‑grained concepts across multiple text‑to‑image systems, as demonstrated by extensive quantitative and user studies.
This article provides a comprehensive overview of AI Agents, tracing their evolution from traditional chatbots to LLM‑driven agents, explaining core components such as perception, reasoning, action, knowledge bases, learning and communication interfaces, and discussing practical use cases, interaction cycles, and future prospects.
This tutorial explains how to deploy the DeepSeek‑r1:1.5b model on a cloud server using Ollama, create a retrieval‑augmented generation (RAG) vector database with the mxbai‑embed‑large embedding model, and build an interactive AI application that answers questions from uploaded PDFs.
GraphRAG, the next generation of Retrieval‑Augmented Generation, combines large language models, knowledge graphs, and graph databases to overcome traditional RAG’s knowledge gaps, hallucinations, and context limitations, and the article reviews its architecture, core modules, a recent 2025 paper, and six notable open‑source implementations.
This guide explains what a knowledge base and Retrieval‑Augmented Generation (RAG) are, why personal knowledge bases are valuable, and provides step‑by‑step instructions for using Cloud Studio's DeepSeek‑R1 CPU template to set up and query a private knowledge base with Open‑WebUI or AnythingLLM.
The guide defines prompts as structured queries that unlock large‑language‑model abilities, outlines five core frameworks (RTF, Chain‑of‑Thought, RISEN, RODES, Density‑Chain), presents two key principles—clear, delimited instructions and explicit reasoning steps—to reduce hallucinations, and surveys advanced techniques such as zero‑shot, few‑shot, RAG, Tree‑of‑Thought and automatic prompt engineering.
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.
The talk outlines Multimodal RAG implementation routes, comparing OCR‑based object recognition, transformer encoder‑decoder encoding, and Visual Language Model processing, explains the ColPali late‑interaction method for multi‑dimensional vector matching, addresses scaling tensors with binarization and reranking, and recommends a hybrid long‑term strategy where VLM excels on abstract imagery while traditional OCR remains valuable.
AIAPI, Baidu’s AI‑native retrieval platform for large language models, tackles hallucination, slow domain updates, and output opacity by delivering authoritative, timely, full‑content data through a dual‑channel architecture that combines traditional search and RAG, employs reusable ranking, graph‑enhanced data layers, dynamic caching that cuts storage by 70 %, and QueryPlan‑based QoS, achieving markedly higher retrieval quality and a 34 % speed gain with Wenxin 4.0.
This comprehensive study evaluates various components of Retrieval‑Augmented Generation pipelines—including query classification, chunking, embedding models, vector databases, retrieval, re‑ranking, summarization, and generator fine‑tuning—identifies optimal configurations, and proposes best‑practice guidelines for both performance‑maximizing and efficiency‑balanced RAG systems.
This article presents a comprehensive analysis of Graph RAG, detailing its implementation workflow, step‑by‑step execution guide, four targeted optimization strategies, and experimental validation that demonstrates significant improvements in global and local question answering for industry scenarios.
At Baidu World 2024, CEO Robin Li unveiled the iRAG retrieval‑augmented image generation model that dramatically reduces hallucinations and introduced the no‑code platform “Miaoda,” showcasing intelligent agents as the next mainstream AI application while highlighting explosive growth in daily model usage.
This article explores the challenges large language models bring to efficiency tools, outlines the AI document assistant's technical thinking and architecture, and details both application‑side and model‑side practices such as retrieval‑augmented generation, intent recognition, and code‑driven table handling, concluding with key lessons.
This article explains the fundamentals of Retrieval‑Augmented Generation (RAG), its four‑step workflow, architecture, and how Intel’s hardware and software optimizations—including vector search, quantized embeddings, and advanced inference extensions—enhance performance, security, and scalability for enterprise LLM applications.
This article introduces GraphRAG, explains the limitations of traditional RAG, outlines four major challenges (fine‑grained retrieval, global context, similarity vs relevance, and macro‑level reasoning), describes GraphRAG’s graph‑based retrieval strategies, showcases comparative experiments, and presents NebulaGraph’s GenAI Suite and RAG products along with future research directions.
The article reflects on Alibaba Cloud's large‑model deployments, outlines four service scenarios, examines three fundamental questions about foundation models, and offers a prioritized roadmap—including prompt engineering, RAG, and organizational changes—to effectively bring LLMs to production.
This article presents over twenty essential Retrieval‑Augmented Generation (RAG) interview questions with detailed answers, covering fundamentals, applications, architecture, training, limitations, ethical considerations, and integration, offering AI enthusiasts and job candidates a comprehensive guide to mastering RAG concepts.
This article reviews the ACL 2024 paper that investigates how large language model‑generated text influences retrieval‑augmented generation pipelines, revealing short‑term retrieval gains but a long‑term “spiral of silence” that marginalizes human‑generated content and homogenizes open‑domain QA results.
This article introduces NVIDIA’s comprehensive solutions for large language models, covering the NeMo Framework’s full‑stack development pipeline, the open‑source TensorRT‑LLM inference accelerator, and Retrieval‑Augmented Generation techniques, while detailing data preprocessing, distributed training, model fine‑tuning, deployment, and performance optimizations.
This article compares GraphRAG with traditional RAG across seven dimensions—suitable scenarios, knowledge representation, retrieval, comprehensive queries, hidden‑relationship understanding, scalability, and performance‑cost trade‑offs—explains how they can be fused, and offers guidance on selecting the right approach for complex data‑driven applications.
The article explains how the R&D data middle platform integrates large language models to automate data collection, real‑time monitoring, intelligent analysis, and rapid root‑cause identification for online issues, detailing the architecture, wide‑table modeling, generative BI, attribution algorithms, RAG enhancements, and future optimization plans.
The paper introduces a method that classifies user queries into ordinary and long‑tail types, applying retrieval‑augmented generation only to long‑tail queries, which improves large language model efficiency and accuracy by leveraging specialized knowledge detection metrics and an extended RAG pipeline.
Deploying large language models faces domain gaps, hallucinations, and high barriers, so Retrieval‑Augmented Generation (RAG) combines retrieval with generation, and advanced optimizations—such as RAPTOR’s hierarchical clustering, Self‑RAG’s self‑reflective retrieval, CRAG’s corrective evaluator, proposition‑level Dense X Retrieval, sophisticated chunking, query rewriting, and hybrid sparse‑dense methods—are essential for improving accuracy, reducing hallucinations, and achieving efficient, scalable performance.
Recent ACL 2024 papers from Alibaba Cloud’s PAI platform showcase open‑source Chinese diffusion models, an interactive multi‑turn prompt generator, a long‑tail knowledge‑aware retrieval‑augmented LLM approach, and a dynamic fusion network for sequential model editing, all integrated into cloud services.
The article explains how recent research tackles large language model hallucinations by combining retrieval‑augmented generation with reinforcement learning, achieving significant accuracy and reliability gains and paving the way for safe AI deployment in critical sectors such as finance and healthcare.
This article presents Task‑aware Decoding (TaD), a plug‑and‑play technique introduced by JD Tech and Tsinghua University and accepted at IJCAI 2024, which reduces intrinsic hallucinations in large language models by comparing pre‑ and post‑fine‑tuning outputs, and demonstrates its effectiveness combined with Retrieval‑Augmented Generation across various tasks.
Mem0 is an open‑source memory‑management middleware for large language models that provides dynamic, context‑aware, and adaptive memory, outperforming traditional Retrieval‑Augmented Generation (RAG) and enabling personalized AI assistants, travel planners, and support agents with concrete Python APIs and examples.
This article reviews the hallucination problem in large language models, analyzes its data, training, and inference sources, and presents Task‑aware Decoding (TaD) and Retrieval‑Augmented Generation (RAG) as effective, plug‑and‑play solutions demonstrated through extensive experiments.
This article reviews the IJCAI 2024‑presented Task‑aware Decoding (TaD) technique, explains how it mitigates large‑language‑model hallucinations when combined with Retrieval‑augmented Generation, and details experimental results, practical deployments, and future research directions.
This article surveys the main challenges of deploying large language models, introduces key RAG optimization papers such as RAPTOR, Self‑RAG, and CRAG, and compiles practical engineering tricks—including chunking, query rewriting, hybrid and progressive retrieval—to help practitioners build more accurate and efficient RAG systems.
This article describes how large language models such as ChatGPT are integrated into courier terminal systems to automate tasks, enhance intelligent voice operations, enable retrieval‑augmented question answering, generate smart prompts, and explore agent‑based workflows, supported by code examples for data extraction, splitting, and embedding.
The article outlines LLM issues such as hallucination, outdated knowledge, and data privacy, then explains Retrieval‑Augmented Generation—detailing its data‑preparation and query‑time retrieval workflow, demonstrates a full LangChain implementation, and contrasts RAG with fine‑tuning as complementary strategies for up‑to‑date, grounded responses.
The DataFun Data Science Summit on May 25 brings together leading experts to present cutting‑edge research on pairwise data causal inference, Retrieval‑Augmented Generation applications, large language model content detection, user growth analytics, and advanced machine‑learning techniques across finance, e‑commerce, and AI domains.
The article examines how large AI models are moving toward a unified paradigm that reduces task‑algorithm coupling, outlines multi‑layer efficiency challenges—from model compression and sparsity to software and infrastructure optimization—and highlights NVIDIA’s GTC 2024 China AI Day sessions showcasing the latest LLM technologies and registration details.
The article explains Retrieval-Augmented Generation (RAG), its workflow, advantages, comparison with fine-tuning, and provides a step-by-step implementation using Baidu's ERNIE SDK, LangChain, and ChromaDB to build a personal knowledge base that answers queries with retrieved context.
This article outlines three practical approaches—direct prompting, retrieval‑augmented generation (RAG), and fine‑tuning—to connect enterprise applications to large language models, explains key prompt‑engineering techniques, details RAG workflow and vector‑database integration, and provides step‑by‑step guidance for fine‑tuning on the KubeAI platform.
This article explains how Retrieval‑Augmented Generation (RAG) can improve LLM‑based Text2SQL conversion, covering RAG fundamentals, LangChain implementation steps, practical enhancements for SQL agents, and future directions for integrating domain knowledge.
The article surveys modern AI agents—software entities powered by large language models that perceive multimodal inputs, reason via brain modules, act through tools or embodied actions, employ retrieval‑augmented generation and chain‑of‑thought planning, and can operate singly (e.g., AutoGPT) or collaboratively via frameworks like Microsoft’s AutoGen—while highlighting current challenges such as controllability, memory limits, parallelism, and reliability.
The article surveys Baidu Search’s intelligent question‑answering system, tracing its evolution from feature‑engineered retrieval to large pre‑trained and generative models, and detailing hierarchical readers, multi‑teacher distillation, retrieval‑enhanced generation, and instruction decomposition as key techniques for delivering fast, accurate, citation‑rich answers.
This article reviews the evolution of machine question answering, from early feature‑engineered systems to modern large‑language‑model‑driven retrieval‑augmented generation, outlines Baidu Search’s current Retriever‑Reader architecture, discusses challenges such as semantic complexity, latency and answer quality, and presents solutions including hierarchical DocMRC modeling, multi‑teacher knowledge distillation, and instruction decomposition for efficient, high‑quality answers.
This guide details a complete end‑to‑end solution for constructing a large‑language‑model knowledge‑base chatbot on Alibaba Cloud, covering background, modular architecture, vector database selection, text preprocessing, embedding models, LLM fine‑tuning, prompt engineering, deployment with PAI‑EAS and BladeLLM, and real‑world results.
This guide explains the Retrieval‑Augmented Generation (RAG) technique, detailing how user queries are matched to private knowledge bases, how relevant passages are retrieved, and how large language models use those passages to generate context‑aware answers, complete with code examples and practical tips.
This article reviews Alibaba DAMO Academy's research on information extraction, covering background concepts, PLM-era extraction paradigms, few‑shot extraction techniques, and the emerging LLM‑era approaches, while also sharing practical insights, benchmark results, and future directions.
This article explains the design and implementation of the Codebase AI Assistant in the Chocolate Factory framework, covering its problem‑solving DSL, retrieval‑augmented generation pipeline, indexing and querying stages, prompt strategies, and code‑splitting rules that together enable efficient semantic code search.
