knowledge retrieval

IT Services Circle

Jun 6, 2025 · Artificial Intelligence

Master Retrieval‑Augmented Generation (RAG): From Basics to Advanced Practices

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.

AIRAGRetrieval-Augmented Generation

0 likes · 12 min read

Master Retrieval‑Augmented Generation (RAG): From Basics to Advanced Practices

Tencent Technical Engineering

May 19, 2025 · Artificial Intelligence

RAG, Agents, and Multimodal Large Models: Evolution, Challenges, and Future Trends

This article examines the evolution of large model technologies—including Retrieval‑Augmented Generation, AI agents, and multimodal models—detailing their technical foundations, practical challenges, industry applications, and future development trends, offering a comprehensive perspective for AI practitioners and researchers.

AI AgentRAGknowledge retrieval

0 likes · 14 min read

RAG, Agents, and Multimodal Large Models: Evolution, Challenges, and Future Trends

DataFunTalk

Apr 24, 2025 · Artificial Intelligence

Is Retrieval‑Augmented Generation (RAG) Dead Yet?

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

AIContext WindowRAG

0 likes · 9 min read

Is Retrieval‑Augmented Generation (RAG) Dead Yet?

DevOps

Apr 2, 2025 · Artificial Intelligence

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

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

Artificial IntelligenceLLMRAG

0 likes · 10 min read

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

Tencent Cloud Developer

Apr 2, 2025 · Artificial Intelligence

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

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

AIAgentic AIHallucination Mitigation

0 likes · 9 min read

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

Cognitive Technology Team

Feb 28, 2025 · Artificial Intelligence

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

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

Artificial IntelligenceDeepSearcherGraphRAG

0 likes · 20 min read

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

DaTaobao Tech

Oct 23, 2024 · Artificial Intelligence

Retrieval-Augmented Generation (RAG): Principles, Applications, Limitations and Challenges

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.

AILLMRAG

0 likes · 13 min read

Retrieval-Augmented Generation (RAG): Principles, Applications, Limitations and Challenges

JD Retail Technology

May 22, 2024 · Artificial Intelligence

AI Multi‑Agent System for E‑commerce: Design, Implementation, and Operational Insights

This article presents a comprehensive overview of JD Retail's AI‑driven multi‑agent architecture for e‑commerce assistance, detailing how real‑world merchant decision processes are modeled with ReAct‑based LLM agents, the hierarchical workflow, training pipelines, monitoring mechanisms, and future directions for scalable intelligent commerce support.

AILLMReact

0 likes · 20 min read

AI Multi‑Agent System for E‑commerce: Design, Implementation, and Operational Insights

Rare Earth Juejin Tech Community

Apr 8, 2024 · Artificial Intelligence

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

The article introduces PreFLMR, an open‑source, general‑purpose pre‑trained multimodal retriever that leverages fine‑grained late‑interaction to boost retrieval‑augmented generation for knowledge‑intensive visual tasks, describes its M2KR benchmark, training stages, and strong experimental results across multiple tasks.

AIFLMRRAG

0 likes · 11 min read

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

Tencent Tech

Sep 20, 2023 · Artificial Intelligence

Why Do Large Language Models Hallucinate and How to Reduce It?

The article explains why large language models generate hallucinations—due to data errors, training conflicts, and inference uncertainty—and outlines data‑cleaning, model‑level feedback, knowledge augmentation, constraint techniques, and post‑processing methods such as the “Truth‑seeking” algorithm to mitigate the issue.

AI safetydata qualityhallucination

0 likes · 8 min read

Why Do Large Language Models Hallucinate and How to Reduce It?

Tencent Cloud Developer

Jun 28, 2023 · Artificial Intelligence

Prompt Engineering: Fundamentals, Techniques, and Advanced Strategies

Prompt engineering teaches how to craft effective instructions, context, input data, and output formats for large language models, using clear commands, iterative refinement, and advanced methods such as zero‑shot, few‑shot, chain‑of‑thought, Tree of Thoughts, retrieval‑augmented and progressive‑hint prompting to achieve precise, reliable results across diverse tasks.

AIChain-of-Thoughtfew-shot learning

0 likes · 17 min read

Prompt Engineering: Fundamentals, Techniques, and Advanced Strategies

DataFunSummit

Feb 24, 2023 · Artificial Intelligence

Baidu PLATO Open‑Domain Dialogue Model: Technology, Challenges, and Applications

The article presents Baidu's PLATO open‑domain dialogue system, detailing its evolution from expert‑rule to retrieval‑based and large‑scale generative models, describing its hidden‑variable architecture, major research challenges such as persona stability, long‑term memory, knowledge accuracy, and showcasing real‑world applications and Q&A from a DataFunSummit2022 livestream.

AIOpen-domain DialoguePLATO

0 likes · 25 min read

Baidu PLATO Open‑Domain Dialogue Model: Technology, Challenges, and Applications