This guide explains how to overcome the limitations of text‑only embeddings for enterprise AI search by using a multimodal embedding model to index and retrieve both text and images, detailing the full workflow, code examples, and performance benefits.
This article provides a comprehensive analysis of large language model hallucinations, detailing their definitions, classifications, root causes, detection techniques, and a wide range of mitigation approaches—including RAG pipelines, decoding strategies, and model‑enhancement methods—to improve reliability and safety in real‑world AI applications.
The talk outlines RAG2.0’s design challenges—low vector recall, complex documents, semantic gaps—and presents a two‑stage architecture using deep multimodal understanding and knowledge‑graph‑enhanced retrieval, detailing advanced chunking, multi‑index and multi‑path retrieval, efficient sorting models like ColBERT, and future multi‑modal and memory‑augmented agent directions.
The article outlines a step‑by‑step methodology for creating Retrieval‑Augmented Generation and custom Agent applications with LangChain, illustrated by an AI assistant for activity components that evolves from a rapid Dify prototype to a LangChain‑based RAG system and finally a hand‑crafted ReAct‑style agent, detailing LCEL chain composition, vector‑search integration, model performance trade‑offs, and a unified routing layer.
This article presents the evolution from traditional to intelligent BI, explores how large language models enable natural‑language data analysis, details the OlaChat platform’s architecture, metadata‑enhanced retrieval methods, Text2SQL pipeline, multi‑turn dialogue system, and shares practical deployment insights and Q&A.
The article details the development of Yunli XiaoZhi, an AI‑driven logistics chatbot that combines knowledge‑base Q&A, data‑analysis, proactive alerts and report‑pushing to streamline SOP access, reduce manual query effort, and improve operational efficiency for operators, carriers and drivers.
Incremental knowledge updates in Retrieval‑Augmented Generation (RAG) systems can be achieved by using document‑level or chunk‑level strategies, leveraging hash fingerprints, record managers, and framework‑specific APIs such as LangChain’s index() with cleanup modes or LlamaIndex’s ingestion pipeline, reducing redundant computation and cost.
In this interview, Perplexity AI CEO Aravind Srinivas explains the company’s retrieval‑augmented generation architecture, multi‑model strategy, vector‑database use, competitive positioning against Google, monetization plans, and future product road‑map, offering a deep industry perspective on AI‑driven search.
Baidu Intelligent Cloud’s Qianfan AppBuilder 3.0 offers an enterprise‑grade platform that simplifies large‑model application development by providing high‑accuracy RAG, robust agent scheduling, extensive integration, secure private‑or‑hybrid deployment, and a guided methodology, enabling industries to transform processes, add AI copilots, and create novel capabilities.
This article reviews Wang et al.'s 2024 research on Retrieval‑Augmented Generation, outlining optimal practices such as query classification, chunk sizing, hybrid metadata search, embedding selection, vector databases, query transformation, reranking, document repacking, summarization, fine‑tuning, and multimodal retrieval to guide developers in constructing high‑performance RAG pipelines.
This article describes Siemens' journey in applying generative AI and Retrieval‑Augmented Generation to create an internal knowledge chatbot, detailing the business challenges, technical architecture, data integration, multi‑modal capabilities, deployment outcomes, and strategic lessons for enterprise AI adoption.
This tutorial walks through constructing a zero‑to‑one RAG pipeline that ingests OSS text files via EventBridge, transforms them into embeddings with DashScope, stores vectors in DashVector, and performs semantic search using FunctionCompute and a Qwen‑Turbo LLM, complete with code samples and configuration steps.
This article reviews the China Academy of Information and Communications Technology's advancements in large‑model‑driven data intelligence, covering development trends, key deployment technologies such as prompt engineering, fine‑tuning and RAG, emerging application paradigms, challenges, and a series of newly drafted standards to guide industry adoption.
This article details Bilibili’s implementation of a large‑language‑model‑driven intelligent assistant for its massive big‑data platform, covering background, problem analysis, architectural design, knowledge‑base construction, precision and recall challenges, deployment across offline and real‑time Spark/Flink diagnostics, and future outlooks.
IBM introduced the Granite 3.0 series, an open‑source family of large language models that combine cutting‑edge performance with enhanced security, multi‑language support, and cost‑efficiency, while offering a variety of base, instruct, and specialist variants for enterprise use.
Retrieval-Augmented Generation (RAG) combines a retriever that fetches relevant external documents and a generator that uses them, improving LLM accuracy, relevance, privacy, and up-to-date information, but faces challenges such as retrieval latency, computational cost, chunking strategies, embedding selection, and system integration complexity.
At the 2024 Alibaba Cloud Yúnxī Conference, senior AI Search expert Xing Shaomin detailed the enterprise‑grade Retrieval‑Augmented Generation (RAG) pipeline, covering critical link architecture, effectiveness, performance, and cost optimizations, as well as practical applications, vector store enhancements, LLM agents, and deployment strategies.
This article introduces Retrieval‑Augmented Generation (RAG) as a solution to the hallucination, freshness, and data‑privacy issues of large language models, details its modular architecture, explains the layered system design and hybrid retrieval pipeline, and shares the practical challenges and engineering tricks encountered when deploying RAG in enterprise office scenarios.
This article introduces the open‑source Spring AI Alibaba framework, explains its background, core features such as chat model abstraction, prompt templates, structured output, function calling, RAG and chat memory, and walks through a complete smart‑ticket‑assistant example with code snippets and deployment guidance.
This article details how PingCAP's three‑person AI Lab leveraged Retrieval‑Augmented Generation (RAG) techniques—including basic RAG, fine‑tuned embeddings, re‑ranking, graph RAG, and agent‑based RAG—to create scalable, multilingual document‑question answering services while addressing large‑scale documentation challenges, model limitations, and user feedback loops.
Redis has launched an enhanced, multi‑threaded query engine that dramatically increases throughput and reduces latency for vector similarity searches, enabling vertical scaling and better support for real‑time RAG applications while maintaining sub‑10 ms response times.
This tutorial walks you through configuring Elasticsearch’s Open Inference API to connect with Alibaba Cloud AI Search, covering setup of text generation, rerank, sparse and dense vector services, and demonstrates end‑to‑end requests with code examples for building RAG and semantic search applications.
Anthropic’s Contextual Retrieval augments traditional RAG with contextual embeddings and BM25, reducing retrieval failure rates by 49% (up to 67% with reranking), improving accuracy across domains, and lowering latency and cost through Claude’s prompt‑caching feature.
Spring AI Alibaba is an open‑source Java framework that integrates Alibaba Cloud's large‑model services with Spring AI, offering high‑level abstractions for chat models, prompts, function calling, RAG, and conversation memory, and includes a complete ticket‑assistant example with code snippets.
This tutorial walks through the end‑to‑end process of creating a data‑assistant application using DB‑GPT V0.6.0, covering prerequisite deployment, knowledge‑base construction, sub‑agent creation, RAG‑based QA, AWEL workflow installation, intent‑recognition knowledge base, and unified multi‑agent orchestration.
This article analyzes the Meta Comprehensive RAG (CRAG) benchmark, detailing its three tasks, evaluation metrics, and the champion DB3 team's end‑to‑end solution that combines data preprocessing, dual‑stage retrieval, prompt engineering, LoRA‑based fine‑tuning, and public data augmentation to achieve top scores across all tasks.
This guide walks through setting up the open‑source Kotaemon framework, configuring Azure Document Intelligence and a visual large model, and implementing code to extract and caption images and tables from PDFs for end‑to‑end multimodal RAG applications.
This article presents five hands‑on AI project ideas—from resume optimization to multimodal search—complete with step‑by‑step instructions, required Python libraries, and code snippets, helping beginners and intermediate developers quickly build valuable AI applications.
This guide walks entry‑level developers through building a logistics‑focused QA bot by first embedding documents for vector similarity search, then adding retrieval‑augmented generation, fine‑tuning a small model, integrating hybrid checks, and optimizing deployment with feedback loops to achieve fast, accurate, out‑of‑scope‑aware answers.
In this interview, Denodo executives discuss the origins, challenges, and future of data fabric and data virtualization, explore how generative AI and retrieval‑augmented generation enhance data management, share customer success stories, and offer strategic insights for enterprises navigating digital transformation.
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.
Spring AI is a Java‑centric framework that unifies access to chat, text‑to‑image, embedding and retrieval‑augmented generation models—including OpenAI, Anthropic and Alibaba’s Tongyi Qianwen—through synchronous or asynchronous APIs, POJO mapping, function calling, vector‑store integration and fluent tooling for rapid AI agent development.
This article explains how to construct a Retrieval‑Augmented Generation pipeline in Rust, covering knowledge‑base creation with Qdrant, model loading and embedding using the candle library, data ingestion, and integration of a Rust‑based inference service based on mistral.rs, while also discussing resource usage and common pitfalls.
This article explains how to construct a Retrieval‑Augmented Generation pipeline in Rust, covering knowledge‑base creation with Qdrant, model loading and embedding generation using candle, and integrating a Rust‑based inference service to answer queries with up‑to‑date external data.
This article presents a systematic, four‑part Prompt engineering framework—role definition, problem description, goal setting, and requirement specification—augmented with RAG, few‑shot examples, memory handling, and model‑parameter tuning, enabling developers to craft high‑quality prompts for large language models across diverse tasks.
This article presents a contextual retrieval technique that combines contextual embeddings and contextual BM25 to reduce RAG miss rates by up to 67%, explains the underlying methods, implementation steps, cost considerations, experimental results, and practical deployment guidance.
This article explains NVIDIA’s end‑to‑end stack for large language models, covering the NeMo Framework for data processing, training, and deployment, the open‑source TensorRT‑LLM inference accelerator, and the Retrieval‑Augmented Generation (RAG) technique that enriches model outputs with external knowledge.
This article describes the design, implementation, and impact of a large‑model‑driven logistics chatbot that unifies knowledge Q&A, data analysis, proactive alerts, and report pushing to streamline operations for functional staff, frontline workers, and managers, dramatically reducing query time and improving decision efficiency.
The document describes the design, implementation, and operational results of Yunli XiaoZhi, an AI‑driven portable knowledge‑base and data‑analysis chatbot that consolidates SOPs, manuals, and real‑time information for logistics staff, using LangChain‑based RAG, vector databases, and large‑model prompting to improve query efficiency, proactive alerts, and reporting across multiple user groups.
This article explains how to set up a lightweight local AI Q&A system using Java, LangChain (and LangChain4J), Ollama for LLM inference, embedding techniques, and a vector database (ChromaDB), covering core concepts, environment preparation, Maven dependencies, and sample code.
This article walks through building a local AI question‑answer system using Java, Ollama, LangChain4J, embeddings, and a Chroma vector database, covering LLM fundamentals, embedding techniques, RAG architecture, setup steps, Maven dependencies, and sample code to retrieve and answer queries.
This article explores how digital twins evolve from design tools to real‑time monitoring platforms, how integrating generative AI and retrieval‑augmented generation (RAG) boosts AI accuracy and situational awareness, and why software teams must adopt these combined technologies to stay ahead in modern operations.
The article presents a universal four‑part prompt template—role, problem description, goal, and requirements—augmented with role definitions, RAG‑based knowledge retrieval, few‑shot examples, memory handling, temperature/top‑p tuning, and automated optimization techniques such as APE, APO, and OPRO, enabling developers to reliably craft high‑quality prompts for LLMs.
iQIYI’s AI‑powered search expands beyond title‑only queries by handling fuzzy role, plot, star, award, and semantic searches, using Chain‑of‑Thought‑generated TIPS, Retrieval‑Augmented Generation with sophisticated indexing, chunking, embedding, reranking, and prompt‑engineering to deliver personalized, accurate video recommendations that boost user engagement.
DB‑GPT is an open‑source AI‑native data‑application framework that provides multi‑model management, Text‑to‑SQL optimization, RAG, multi‑agent collaboration, and intelligent workflow orchestration, enabling developers to build scalable large‑model database applications, with proven enterprise adoption, community growth, and academic publications.
This article explains how to extend large language models with domain‑specific knowledge using Retrieval‑Augmented Generation (RAG) in LangChain4j, covering the concepts of RAG, its indexing and retrieval stages, simple RAG setup, detailed API usage, and advanced customization options such as query transformers and content injectors.
This article details an engineering‑focused practice that uses large language models, RAG, prompt engineering, and reranking to automatically generate, review, and refine Aviator scripts for decision‑center policies without any model pre‑training, offering practical insights and code examples for developers.
The talk outlines the evolution of intelligent customer service over three decades, explains how generative large-model AI like ChatGPT has raised service expectations, and presents Alibaba Cloud’s four-stage implementation—experience, efficiency, capability, and insight—through three concrete cases and a roadmap for SMEs to build their own smart service systems.
The article compares traditional rule‑engine architectures with AI‑driven models, explains their differing characteristics, outlines when deterministic rule matching is preferable over flexible AI inference, and recommends practical technologies such as Drools for rule‑based solutions and LLM‑based RAG/Agent frameworks for AI‑centric scenarios.
LangChain4j streamlines the integration of large language models into Java applications by offering a standardized API, extensive support for over a dozen LLM providers and vector stores, a rich toolbox for RAG, chat memory, and tool calling, plus two abstraction layers that cater to both low‑level control and high‑level convenience.
This article explains how to set up FastGPT, a Retrieval‑Augmented Generation (RAG) knowledge‑base system powered by large language models, covering both Docker‑compose image deployment and source‑code installation, including environment configuration, database setup, and API usage examples.
This guide explains vector embeddings, OpenAI's text‑embedding models, and how to use PyMilvus with Zilliz Cloud to create a Retrieval‑Augmented Generation (RAG) system for fast, accurate semantic search.
The article outlines fifteen advanced Retrieval‑Augmented Generation (RAG) techniques—from hierarchical indexing and context caching to multimodal alignment and microservice orchestration—explaining how they help transform AI prototypes into scalable, reliable production systems while highlighting common pitfalls and a concluding call to action.
This article explains the core concepts of Retrieval‑Augmented Generation (RAG), walks through its implementation steps with LangChain.js—including text chunking, embedding, storage, retrieval, and generation—and showcases practical use cases, challenges, and best practices for building reliable AI‑powered applications.
This article examines the 70‑year evolution of databases, explains how large‑model AI drives the rise of vector databases and Retrieval‑Augmented Generation (RAG), outlines the four‑stage RAG workflow, compares Baidu’s self‑built VectorDB with open‑source alternatives, and showcases real‑world deployments that highlight performance, scalability, and enterprise benefits.
Learn step‑by‑step how to integrate OpenSearch LLM’s intelligent Q&A edition with the Dify large‑model platform to create a robust Retrieval‑Augmented Generation (RAG) system, covering architecture, workflow setup, API authentication, result parsing, and practical code examples.
This article details Alibaba Cloud Elasticsearch's AI search advancements, covering embedding vectors, HNSW-based approximate nearest neighbor search, hardware-accelerated vector engines, sparse vectors, hybrid retrieval, the Inference API, and RAG implementations that together boost performance, efficiency, and relevance for modern AI-driven search applications.
This article examines the typical business and technical architectures for large language model applications, covering AI Embedded, Copilot, and Agent modes, single‑ and multi‑agent systems, core frameworks, and guidance on selecting appropriate technical routes.
This article presents a comprehensive overview of 360's document cloud knowledge management and Q&A scenarios, discussing business pain points, large‑model challenges, the advantages of the intelligent document solution, and how knowledge graphs enhance retrieval‑augmented generation and document standardization for AI‑driven enterprise applications.
This article reviews the 70‑year evolution of databases, explains how vector databases and Retrieval‑Augmented Generation (RAG) are reshaping AI applications, and details Baidu Intelligent Cloud's VectorDB architecture, performance advantages, real‑world use cases, and future trends in data engineering.
This article explains how Elasticsearch’s advanced search capabilities—including vector and semantic search, hardware acceleration, hybrid retrieval, model re‑ranking, multi‑vector support, and integrated security—enable robust RAG implementations and outlines future directions such as a new compute engine, stronger vector engines, and cloud‑native serverless deployment.
This article introduces Kotaemon, an open‑source Retrieval‑Augmented Generation platform that lets users chat with their documents, offering a self‑hosted web UI, support for local and API LLMs, hybrid retrieval, multimodal question answering, GraphRAG indexing, and advanced reasoning capabilities, along with step‑by‑step installation via App or Docker.
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.
This article explains why generic large language models struggle with domain‑specific data, introduces Retrieval‑Augmented Generation (RAG) as a solution, compares Word and PDF formats, outlines document‑parsing pipelines, reviews open‑source PDF tools, and presents Alibaba Cloud's rule‑based parsing architecture with performance results.
This article provides a comprehensive overview of large language models, covering their fundamental concepts, historical milestones, parameter scaling, prompt engineering techniques, retrieval‑augmented generation, autonomous agents, and multimodal model applications, illustrating how these technologies reshape AI capabilities across domains.
This article presents eight practical optimization recommendations for the indexing stage of enterprise‑level Retrieval‑Augmented Generation (RAG) applications, covering chunk creation, abbreviation handling, multimodal document processing, semantic enrichment, metadata usage, alternative index types, and embedding model selection.
This article details Tencent Game's end‑to‑end approach to building intelligent NPCs, covering the opportunities brought by AI, the practical implementation of multimodal LLM‑driven dialogue, knowledge‑augmented retrieval, long‑context handling, safety measures, multimodal expression (voice and facial animation), and system‑level performance optimizations for real‑time deployment.
This article explains the necessity of vector databases for AI workloads, reviews the PostgreSQL ecosystem, compares vector database options, provides detailed PgVector installation and usage steps, shares operational best‑practices, performance tuning tips, and real‑world deployment cases at Qunar and Tujia.
This article explores how mature testing teams can leverage large language models for automated code review, outlining the advantages, challenges, and a practical implementation using FastGPT and GitLab CI to build a low‑cost, AI‑enhanced review system that improves efficiency and feedback quality.
This article presents a comprehensive technical overview of how large AI models are reshaping financial data production, governance, multimodal document understanding, lakehouse storage, private‑domain model deployment, data‑centric engineering methods, and multi‑agent intelligent advisory within the finance sector.
Shire 0.5 introduces SonarQube issue support, Git‑enabled ShireQL queries, a reranking function for RAG, and new data‑guarding capabilities like the redact function and customizable secret‑pattern YAML, enabling developers to securely build AI‑powered coding agents that leverage IDE assets while protecting sensitive information.
This article explains how HuoLala leverages large language models, multimodal feature integration, and retrieval‑augmented generation to automatically determine responsibility for order cancellations, improving accuracy, explainability, and driver‑user experience.
RAG technology leverages vector databases to provide context‑aware answers without updating model parameters, and this article explores how cloud search teams integrate multiple vector algorithms, balance cost, stability and latency, and adopt open‑source solutions like OpenSearch to build scalable, enterprise‑grade retrieval systems.
This article shares engineering insights from Hema’s AI smart customer service deployment, detailing key stability factors for AI agents—including hallucination mitigation, memory integration, RAG enhancement, exception handling, and comprehensive monitoring—to improve reliability and performance in real‑world e‑commerce chatbot scenarios.
Redis has launched a multi‑threaded query engine that vertically scales its in‑memory database, dramatically increasing query throughput and lowering latency for vector similarity searches, thereby addressing the performance demands of real‑time retrieval‑augmented generation in generative AI applications.
Redis has introduced a multithreaded query engine that dramatically boosts throughput and lowers latency for vector searches, enabling scalable, real‑time retrieval‑augmented generation (RAG) workloads while preserving the low‑latency performance of its core in‑memory database.
This guide explains how to query a Microsoft GraphRAG knowledge graph using the official CLI, API, and a custom Neo4j implementation, covering both local and global retrieval modes, vector index creation, Cypher query customization, and integration with LangChain for end‑to‑end RAG pipelines.
This article compares the high costs and technical challenges of building a private large language model with the benefits, flexibility, and lower risk of using Retrieval‑Augmented Generation (RAG) on a general LLM, offering practical guidance for testing teams seeking AI assistance.
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.
The article demonstrates how to improve backend menu navigation by building a knowledge base of menu metadata, generating concise Chinese descriptions with OpenAI embeddings, and implementing RAG retrieval using both LangChain code orchestration and DIFY’s visual workflow, highlighting each approach’s flexibility and ease of use.
This article explains the fundamentals of Microsoft GraphRAG, details its indexing pipeline—including text chunking, entity‑relationship extraction, community detection, and description generation—shows how to set up the graphrag library, create adaptive prompts, build the index, and import the resulting graph into Neo4j for visualization and analysis.
In this article, 58.com AI Lab senior director Zhan Kunlin explains how the company built a multi‑layer AI platform, created a vertical large‑language model called LingXi, and developed an AI Agent system with RAG capabilities to accelerate practical AI applications across various business scenarios.
This case study shows how AIGC, implemented through Dify’s low‑code platform, enables a natural‑language AI assistant to recommend and insert the optimal components from a 200‑plus library, streamlining selection, building an embedding‑based knowledge base, exposing a RAG‑driven agent via API, and demonstrating rapid AI‑business validation compared with custom frameworks.
This article details Alibaba Cloud's end‑to‑end architecture for search and advertising recommendation, covering the data platform, AI services, feature‑store design, training and inference optimizations, and the integration of large language models for new recommendation scenarios.
This article explains how to quickly develop an AI‑native application using Spring AI, covering core features such as chat models, prompt templates, function calling, structured output, image generation, embedding, vector stores, and Retrieval‑Augmented Generation (RAG), and provides end‑to‑end Java code examples for building a simple AI‑driven service.
This presentation details Alibaba Cloud's AI platform, covering the end‑to‑end pipeline from big‑data processing and feature engineering to large‑model training, inference optimization, recommendation system architecture, and RAG applications, highlighting practical engineering solutions and performance gains.
Over the past year our team explored applying large language models to specialized domains, detailing their professional benefits, unique challenges such as accuracy and knowledge‑base maintenance, and presenting solutions like alignment enhancement via BPO, Text2API, RAG, and advanced SFT/DPO techniques.
This article explains the fundamentals of large language models, artificial general intelligence, AI-generated content, AI agents, retrieval‑augmented generation, knowledge bases, multimodal processing, fine‑tuning, alignment, tokens, vectors, and related tools, highlighting their capabilities, limitations, and practical considerations.
Galileo’s Hallucination Index evaluates 22 leading generative AI models using a contextual‑adherence metric, ranking Claude 3.5 Sonnet as the overall RAG leader, Gemini 1.5 Flash as the most cost‑effective, and highlighting open‑source and context‑length performance nuances for AI practitioners.
This guide presents a comprehensive AI learning roadmap, dividing study into five progressive stages—from foundational math and programming to core deep‑learning and reinforcement‑learning techniques, large‑model training, industry applications, and future trends—plus curated book lists, tool recommendations, and practical RAG tutorials.
This guide walks readers from beginner to proficient Prompt Engineer by outlining the evolution of prompting, introducing a universal four‑component template, and detailing a five‑step workflow—including refinement, retrieval‑augmented generation, chain‑of‑thought reasoning, and advanced tuning techniques—plus evaluation metrics for LLM performance.
This article explains how to use the Shire language to create reliable coding agents by defining custom RAG workflows, leveraging IDE APIs, code verification functions, and vector‑based search, with detailed examples, configuration snippets, and a roadmap for future enhancements.
Alibaba’s logistics AI team shares their year‑long journey building a vertical‑domain large language model for logistics, detailing model alignment, Text2API, RAG, SFT techniques, challenges like accuracy and knowledge‑base maintenance, and showcasing real‑world applications such as chatbots, DingTalk assistants, and custom AI assistants.
This article provides a comprehensive overview of large language models (LLMs), covering their basic concepts, relationship with NLP, development history, parameter scaling, offline deployment, practical applications, prompt‑engineering frameworks, retrieval‑augmented generation, LangChain integration, agents, workflow orchestration, and future directions toward multimodal AI and AGI.
Vertical domain large language models, fine‑tuned on specialized data, deliver higher expertise and task performance, but require continual knowledge updates and careful alignment; techniques such as BPO‑guided instruction tuning (+1.8% accuracy), Reflexion‑based Text2API (+4% API correctness), advanced RAG preprocessing, and SFT combined with ORPO (+5.2% gain) demonstrate notable improvements while underscoring remaining challenges and collaborative opportunities.
Exploring Large Language Models, this article surveys their core concepts, evolution through Transformers, GPT and BERT, generation challenges, diverse applications such as QA, multimodal creation, summarization and retrieval‑augmented generation, prompt‑engineering frameworks and tools, LangChain‑based pipelines, AI‑driven agents, and future prospects toward domain‑specific use, multimodality, and AGI.
TaD, a task‑aware decoding technique jointly developed by JD.com and Tsinghua University and presented at IJCAI 2024, leverages differences between pre‑ and post‑fine‑tuned LLM outputs to construct knowledge vectors, significantly reducing hallucinations across various models, tasks, and data‑scarce scenarios, especially when combined with RAG.
This article provides a comprehensive, step‑by‑step guide for developing large‑language‑model (LLM) applications, covering prompt design techniques, n‑shot and chain‑of‑thought strategies, retrieval‑augmented generation, structured I/O, workflow optimization, evaluation pipelines, operational best practices, and team organization to create reliable, scalable AI products.
This article examines how the AI‑Native approach reshapes the AISWare CEM platform by integrating large language models, Retrieval‑Augmented Generation, and atomic capability decomposition to improve user perception, streamline interactions, and enable intelligent diagnostic assistants for telecom operators.
This article provides a step‑by‑step guide for Java engineers on building a Retrieval‑Augmented Generation (RAG) application using the LangChain4j framework, covering RAG fundamentals, environment setup, Maven integration, document loading, splitting, embedding with OpenAI, vector store management with Chroma, and prompt‑based LLM interaction.
