RAG | BestHub

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167 articles

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DevOps

Apr 17, 2024 · Artificial Intelligence

Engineering Capabilities for Enterprise Large Model Applications: Prompt Engineering, RAG, and Fine‑Tuning

The article explores how enterprises can build and improve large‑model applications by combining prompt engineering, retrieval‑augmented generation (RAG), and fine‑tuning, discusses their relationships, optimization dimensions, testing challenges, and provides practical guidance for SE4AI implementation.

AI EngineeringFine‑tuningPrompt Engineering

0 likes · 20 min read

Engineering Capabilities for Enterprise Large Model Applications: Prompt Engineering, RAG, and Fine‑Tuning

DataFunTalk

Feb 11, 2025 · Artificial Intelligence

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

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

AI evaluationRAGknowledge engineering

0 likes · 9 min read

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

DataFunTalk

Jan 6, 2025 · Artificial Intelligence

Building and Applying NIO's Enterprise Knowledge Platform: Architecture, Challenges, and Future Directions

This article presents a comprehensive overview of NIO's company‑wide knowledge platform, detailing its background, layered architecture, retrieval‑augmented generation workflow, challenges such as accuracy, permission control and high concurrency, and future plans for AI‑assisted understanding, creation, multimodal capabilities, and expanded knowledge types.

AIRAGRetrieval-Augmented Generation

0 likes · 18 min read

Building and Applying NIO's Enterprise Knowledge Platform: Architecture, Challenges, and Future Directions

DataFunTalk

Dec 10, 2024 · Artificial Intelligence

Tencent Large Language Model Applications: RAG, GraphRAG, and Agent Technologies

This article explores Tencent's large language model deployments across various business scenarios, detailing the principles and practical implementations of Retrieval‑Augmented Generation (RAG), GraphRAG for role‑playing, and Agent technologies, while also covering model fine‑tuning, knowledge‑base construction, and evaluation methods.

AI applicationsAgentGraphRAG

0 likes · 15 min read

Tencent Large Language Model Applications: RAG, GraphRAG, and Agent Technologies

DataFunTalk

Oct 31, 2024 · Artificial Intelligence

Tencent OlaChat: An LLM‑Powered Intelligent Business Intelligence Platform – Architecture, Capabilities, and Practice

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.

AI agentsLLMMetadata Retrieval

0 likes · 20 min read

Tencent OlaChat: An LLM‑Powered Intelligent Business Intelligence Platform – Architecture, Capabilities, and Practice

DataFunTalk

Oct 9, 2024 · Artificial Intelligence

Interview on Data Fabric, Data Virtualization, and AI Integration with Denodo Leaders

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.

Data FabricData VirtualizationDenodo

0 likes · 19 min read

Interview on Data Fabric, Data Virtualization, and AI Integration with Denodo Leaders

DataFunSummit

Apr 7, 2025 · Artificial Intelligence

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

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

AI applicationsAgentsData Governance

0 likes · 8 min read

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

DataFunSummit

Feb 21, 2025 · Artificial Intelligence

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

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

Document UnderstandingRAGTensor Retrieval

0 likes · 11 min read

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

DataFunSummit

Jan 8, 2025 · Artificial Intelligence

Ximalaya's ChatBI: Applying Large‑Model AI to Build an Intelligent Business Intelligence Platform

This article presents Ximalaya's practical exploration of a large‑model‑driven BI system called ChatBI, detailing the background challenges, product architecture, implementation workflow, model‑optimization techniques, launch results, and future directions for data‑intelligent operations.

AI agentsData IntelligencePrompt Engineering

0 likes · 9 min read

Ximalaya's ChatBI: Applying Large‑Model AI to Build an Intelligent Business Intelligence Platform

DataFunSummit

Nov 8, 2024 · Artificial Intelligence

ChatDBA: An AI‑Powered Database Fault Diagnosis Assistant Using Retrieval‑Augmented Generation

ChatDBA, developed by Shanghai Aikesheng, is an AI-driven database operation assistant that leverages large language models and Retrieval‑Augmented Generation to provide fault diagnosis, knowledge learning, SQL generation and optimization, addressing challenges such as vague outputs, complex troubleshooting logic, and memory management through a structured architecture and multi‑modal retrieval strategies.

AIDatabaseFault Diagnosis

0 likes · 10 min read

ChatDBA: An AI‑Powered Database Fault Diagnosis Assistant Using Retrieval‑Augmented Generation

DataFunSummit

Oct 25, 2024 · Artificial Intelligence

Progress and Standardization of Large Model + Data Intelligence Applications by the China Academy of Information and Communications Technology

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.

AIData IntelligenceRAG

0 likes · 13 min read

Progress and Standardization of Large Model + Data Intelligence Applications by the China Academy of Information and Communications Technology

DataFunSummit

Oct 24, 2024 · Big Data

Bilibili’s Large Language Model‑Based Intelligent Assistant for the Big Data Platform: Architecture, Principles, and Deployment

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.

AgentBig DataFlink

0 likes · 23 min read

Bilibili’s Large Language Model‑Based Intelligent Assistant for the Big Data Platform: Architecture, Principles, and Deployment

DataFunSummit

Oct 21, 2024 · Artificial Intelligence

Retrieval‑Augmented Generation (RAG) for Office Applications: Architecture, Challenges, and Practical Practices

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.

AIPrompt EngineeringRAG

0 likes · 19 min read

Retrieval‑Augmented Generation (RAG) for Office Applications: Architecture, Challenges, and Practical Practices

DataFunSummit

Oct 18, 2024 · Artificial Intelligence

Building Efficient RAG Applications with a Small Team: Insights from PingCAP AI Lab

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.

AgentFine‑tuningLLM

0 likes · 14 min read

Building Efficient RAG Applications with a Small Team: Insights from PingCAP AI Lab

DataFunSummit

Oct 2, 2024 · Artificial Intelligence

NVIDIA’s Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation

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.

AI accelerationNVIDIANeMo

0 likes · 17 min read

NVIDIA’s Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation

DataFunSummit

Sep 6, 2024 · Artificial Intelligence

Knowledge Graph and RAG Applications in 360 Document Cloud: Challenges and Solutions

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.

AIDocument ManagementRAG

0 likes · 15 min read

Knowledge Graph and RAG Applications in 360 Document Cloud: Challenges and Solutions

DataFunSummit

Sep 4, 2024 · Artificial Intelligence

How Elasticsearch Powers Retrieval‑Augmented Generation (RAG) Applications

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.

AIElasticsearchHybrid Search

0 likes · 9 min read

How Elasticsearch Powers Retrieval‑Augmented Generation (RAG) Applications

DataFunSummit

Aug 25, 2024 · Artificial Intelligence

Applying Large AI Models to Financial Data Governance and Innovative Use Cases

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.

AIData GovernanceMulti-Agent

0 likes · 21 min read

Applying Large AI Models to Financial Data Governance and Innovative Use Cases

DataFunTalk

Aug 2, 2024 · Artificial Intelligence

From Big Data to Large Models: Alibaba Cloud AI Platform Architecture and Practices for Search Recommendation

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.

AI PlatformBig DataFeature Store

0 likes · 18 min read

From Big Data to Large Models: Alibaba Cloud AI Platform Architecture and Practices for Search Recommendation

DataFunTalk

Jun 21, 2024 · Artificial Intelligence

Fine‑tuning Large Language Models with Alibaba Cloud PAI: Practices, Techniques, and Deployment

This article introduces the Alibaba Cloud PAI platform for large language model (LLM) fine‑tuning, covering model‑training pipelines, performance‑cost trade‑offs, retrieval‑augmented generation, fine‑tuning methods such as full‑parameter, LoRA and QLoRA, model selection, data preparation, evaluation, and real‑world deployment examples.

AI PlatformFine‑tuningLLM

0 likes · 20 min read

Fine‑tuning Large Language Models with Alibaba Cloud PAI: Practices, Techniques, and Deployment