retrieval-augmented generation — 15 Technical Articles

Feb 26, 2026 · Artificial Intelligence

How RAG Can Overcome Large‑Model Pitfalls in Enterprise Knowledge Work

This article explains the challenges large language models face in real‑world applications, introduces Retrieval‑Augmented Generation (RAG) as a solution, and details a modular RAG architecture, its components, and practical techniques for document parsing, query rewriting, hybrid retrieval, ranking, and answer generation in an enterprise setting.

Document ParsingLLM deploymentRAG

0 likes · 22 min read

How RAG Can Overcome Large‑Model Pitfalls in Enterprise Knowledge Work

Data Party THU

Feb 15, 2026 · Artificial Intelligence

Why Retrieval‑Augmented Generation Is Still Fragile: Boosting Generalization and Evidence‑Based Answers

Although modern information access is faster than ever, retrieval‑augmented generation systems remain vulnerable, especially when faced with distribution shifts, making it crucial to improve both retriever generalization across domains and languages and ensure generators produce evidence‑grounded responses or refuse when evidence is lacking.

AI robustnessRAGevidence grounding

0 likes · 3 min read

Why Retrieval‑Augmented Generation Is Still Fragile: Boosting Generalization and Evidence‑Based Answers

Wu Shixiong's Large Model Academy

Nov 22, 2025 · Artificial Intelligence

Why Your RAG System Slows Down Over Time and How to Fix It

The article explains why a production Retrieval‑Augmented Generation (RAG) system becomes slower as it runs—due to growing embedding costs, expanding vector databases, heavier re‑ranking, and larger prompts—and provides concrete engineering optimizations such as batching, async concurrency, caching, partitioned retrieval, HNSW tuning, replica scaling, answer caching, and prompt sparsification to keep performance stable.

AI engineeringPerformance optimizationRAG

0 likes · 10 min read

Why Your RAG System Slows Down Over Time and How to Fix It

Data Party THU

Oct 11, 2025 · Artificial Intelligence

From Transformers to LLaMA 4: A Journey Through the Biggest LLMs

This article surveys the most influential large language models released since 2017, detailing the core innovations of Transformer, BERT, GPT series, T5, Retrieval‑Augmented Generation, and the latest LLaMA and Meta models, while highlighting their architectures, training paradigms, and impact on NLP research.

LLMlarge language modelsmodel scaling

0 likes · 21 min read

From Transformers to LLaMA 4: A Journey Through the Biggest LLMs

JD Tech

Oct 9, 2025 · Artificial Intelligence

What Is Retrieval‑Augmented Generation (RAG) and How Does It Boost AI Accuracy?

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.

ChunkingLLMRAG

0 likes · 14 min read

What Is Retrieval‑Augmented Generation (RAG) and How Does It Boost AI Accuracy?

JD Tech Talk

Sep 28, 2025 · Artificial Intelligence

What Is Retrieval‑Augmented Generation (RAG) and How Does It Power Modern AI?

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.

AIChunkingEmbedding

0 likes · 15 min read

What Is Retrieval‑Augmented Generation (RAG) and How Does It Power Modern AI?

Volcano Engine Developer Services

Sep 11, 2025 · Artificial Intelligence

Why Do Large Language Models Hallucinate? Causes, Types, and Mitigation Strategies

This article examines the growing problem of hallucinations in large language models, outlining their causes across the model lifecycle, classifying four main hallucination types, and presenting both retrieval‑augmented generation and detection techniques—white‑box and black‑box—to reduce factual errors in critical applications.

AI safetyLLMhallucination

0 likes · 15 min read

Why Do Large Language Models Hallucinate? Causes, Types, and Mitigation Strategies

JD Retail Technology

Jul 21, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

This article presents a comprehensive approach that combines causal inference, large language models, and retrieval‑augmented generation to automate e‑commerce price recommendation, detailing the three‑step workflow, challenges across product categories, the RAG architecture, process‑reward‑guided tree search, reinforcement learning refinements, and experimental results showing significant accuracy and speed improvements.

causal inferencechain of thoughte‑commerce pricing

0 likes · 16 min read

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

Baobao Algorithm Notes

Jul 18, 2025 · Artificial Intelligence

30+ Expert Q&A on Large Language Model Architecture, Training, and Deployment

This article compiles more than thirty interview‑style questions and detailed answers covering large‑model fundamentals such as encoder‑decoder trade‑offs, self‑attention versus RNN, context length, tokenization, embedding strategies, FlashAttention, RoPE, prompt design, retrieval‑augmented generation, safety measures, fine‑tuning, and model distillation, providing a comprehensive technical reference for practitioners.

Attention Mechanismretrieval-augmented generation

0 likes · 53 min read

30+ Expert Q&A on Large Language Model Architecture, Training, and Deployment

Fun with Large Models

Jun 27, 2025 · Artificial Intelligence

Boost Answer Accuracy: Detailed GraphRAG Retrieval Steps with Knowledge Graphs

This article walks through GraphRAG’s retrieval phase, showing how knowledge‑graph entities, relationships, and community reports are assembled into a query context, comparing local and global modes with traditional RAG, and illustrating the process with a concrete “Age of Big Data” example.

GraphRAGKnowledge GraphLLM

0 likes · 14 min read

Boost Answer Accuracy: Detailed GraphRAG Retrieval Steps with Knowledge Graphs

Fun with Large Models

Jun 19, 2025 · Artificial Intelligence

How GraphRAG Boosts Answer Accuracy with Knowledge Graphs (Part 1)

This article explains GraphRAG’s architecture, compares it with traditional RAG, and presents experimental results showing that GraphRAG’s knowledge‑graph‑driven retrieval markedly improves answer accuracy, especially on low‑match, multi‑paragraph queries.

GraphRAGKnowledge GraphRAG

0 likes · 11 min read

How GraphRAG Boosts Answer Accuracy with Knowledge Graphs (Part 1)

Alibaba Cloud Developer

Dec 4, 2024 · Artificial Intelligence

How Alibaba’s GTE‑Multilingual Models Boost RAG with Long‑Doc and Multi‑Language Support

Alibaba's Tongyi Lab introduces the GTE‑Multilingual series, high‑performance encoder‑only models that support 8k‑token texts, 75 languages, elastic and sparse embeddings, and demonstrate superior retrieval‑augmented generation performance across multilingual and long‑document benchmarks.

AI model trainingSparse Embeddingelastic embedding

0 likes · 18 min read

How Alibaba’s GTE‑Multilingual Models Boost RAG with Long‑Doc and Multi‑Language Support

Alibaba Cloud Developer

Nov 18, 2024 · Artificial Intelligence

Solving Knowledge Challenges in Retrieval‑Augmented Generation: Practical Optimizations

This article shares a half‑year of hands‑on experience with Retrieval‑Augmented Generation, analyzing why simple RAG setups often feel unintelligent, identifying three core knowledge issues, and presenting concrete optimization strategies—including chunking, knowledge expansion, and tag‑based conflict resolution—to improve retrieval and generation performance in low‑resource environments.

AIInformation RetrievalRAG

0 likes · 25 min read

Solving Knowledge Challenges in Retrieval‑Augmented Generation: Practical Optimizations

NewBeeNLP

Jun 24, 2024 · Artificial Intelligence

How Domain Large Models Are Shaping the Future of AI: Challenges and Solutions

This article reviews Fudan University's Knowledge Factory Lab research on domain large models, covering background, three major deployment challenges, data‑selection strategies, ability‑enhancement techniques, collaborative workflows, and retrieval‑augmented generation methods that aim to make large models practical for real‑world tasks.

Domain AdaptationKnowledge ExtractionModel Alignment

0 likes · 18 min read

How Domain Large Models Are Shaping the Future of AI: Challenges and Solutions

DataFunTalk

Jun 15, 2024 · Artificial Intelligence

Research on Domain Large Models by Fudan University Knowledge Factory Lab

This article presents Fudan University's Knowledge Factory Lab research on domain large models, covering background, challenges, data selection, source‑enhanced tagging, capability improvements, self‑correction, collaborative workflows, and retrieval‑augmented generation for practical AI deployment.

AI researchDomain AdaptationKnowledge Graph

0 likes · 16 min read

Research on Domain Large Models by Fudan University Knowledge Factory Lab