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DaTaobao Tech
DaTaobao Tech
Mar 19, 2025 · Artificial Intelligence

Retrieval Augmented Generation (RAG): Principles, Challenges, and Implementation Techniques

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.

LLMRAGRetrieval Augmented Generation
0 likes · 27 min read
Retrieval Augmented Generation (RAG): Principles, Challenges, and Implementation Techniques
AIWalker
AIWalker
Mar 18, 2025 · Artificial Intelligence

How ImageRAG Boosts Text‑to‑Image Generation with Retrieval‑Augmented Generation

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.

AI GenerationBenchmarkImageRAG
0 likes · 17 min read
How ImageRAG Boosts Text‑to‑Image Generation with Retrieval‑Augmented Generation
AI Algorithm Path
AI Algorithm Path
Mar 11, 2025 · Artificial Intelligence

AI Agents Overview: Foundations, Core Components, and When to Use Them

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.

AI agentsRetrieval Augmented GenerationTool Use
0 likes · 15 min read
AI Agents Overview: Foundations, Core Components, and When to Use Them
Ma Wei Says
Ma Wei Says
Feb 25, 2025 · Artificial Intelligence

What Is GraphRAG? A Deep Dive into Next‑Gen Retrieval‑Augmented Generation and Open‑Source Implementations

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.

GraphRAGRetrieval Augmented Generationartificial intelligence
0 likes · 9 min read
What Is GraphRAG? A Deep Dive into Next‑Gen Retrieval‑Augmented Generation and Open‑Source Implementations
Tencent Technical Engineering
Tencent Technical Engineering
Feb 17, 2025 · Artificial Intelligence

Prompt Engineering: Definitions, Frameworks, Principles, and Advanced Techniques

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.

AIRetrieval Augmented Generationchain-of-thought
0 likes · 29 min read
Prompt Engineering: Definitions, Frameworks, Principles, and Advanced Techniques
DataFunSummit
DataFunSummit
Jan 22, 2025 · Artificial Intelligence

RAG2.0 Engine Design Challenges and Implementation

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.

Hybrid SearchRAGRetrieval Augmented Generation
0 likes · 23 min read
RAG2.0 Engine Design Challenges and Implementation
Sohu Tech Products
Sohu Tech Products
Jan 8, 2025 · Artificial Intelligence

Multimodal RAG: Implementation Paths and Development Prospects

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.

ColPaliDocument ProcessingMultimodal RAG
0 likes · 10 min read
Multimodal RAG: Implementation Paths and Development Prospects
Baidu Geek Talk
Baidu Geek Talk
Dec 16, 2024 · Artificial Intelligence

AIAPI: Baidu's AI-Native Retrieval System for Large Language Model Applications

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.

AI-Native SystemsAIAPIQuery Planning
0 likes · 12 min read
AIAPI: Baidu's AI-Native Retrieval System for Large Language Model Applications
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 15, 2024 · Artificial Intelligence

What Are the Best Practices for Retrieval‑Augmented Generation (RAG)?

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.

Fine-tuningLLMRAG
0 likes · 17 min read
What Are the Best Practices for Retrieval‑Augmented Generation (RAG)?
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Dec 13, 2024 · Artificial Intelligence

Optimizing Graph RAG: Boosting Global QA with Better Chunking, Prompts, and Entity Extraction

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.

Graph RAGLLM optimizationPrompt engineering
0 likes · 18 min read
Optimizing Graph RAG: Boosting Global QA with Better Chunking, Prompts, and Entity Extraction
Baidu Tech Salon
Baidu Tech Salon
Nov 13, 2024 · Industry Insights

Baidu’s iRAG and “Miaoda”: Solving AI Hallucinations and Powering the No‑Code Revolution

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.

AIIntelligent agentsNo-code
0 likes · 11 min read
Baidu’s iRAG and “Miaoda”: Solving AI Hallucinations and Powering the No‑Code Revolution
Tencent Docs Tech Team
Tencent Docs Tech Team
Nov 13, 2024 · Artificial Intelligence

Technical Architecture and Practices of the AI Document Assistant

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.

AIAI ArchitectureDocument Automation
0 likes · 16 min read
Technical Architecture and Practices of the AI Document Assistant
Architects' Tech Alliance
Architects' Tech Alliance
Nov 12, 2024 · Artificial Intelligence

How Retrieval‑Augmented Generation Boosts Enterprise AI with Intel Optimizations

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.

AI inferenceEmbedding QuantizationIntel Optimization
0 likes · 14 min read
How Retrieval‑Augmented Generation Boosts Enterprise AI with Intel Optimizations
DataFunSummit
DataFunSummit
Nov 9, 2024 · Artificial Intelligence

GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments

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.

AIGraphRAGRetrieval Augmented Generation
0 likes · 16 min read
GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments
Fighter's World
Fighter's World
Oct 26, 2024 · Artificial Intelligence

Key Considerations for Deploying Large Language Models in Cloud Services

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.

AI deploymentAlibaba CloudCloud Services
0 likes · 8 min read
Key Considerations for Deploying Large Language Models in Cloud Services
DevOps
DevOps
Oct 8, 2024 · Artificial Intelligence

Top 20+ Retrieval‑Augmented Generation (RAG) Interview Questions and Answers

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.

AI InterviewNLPRAG
0 likes · 15 min read
Top 20+ Retrieval‑Augmented Generation (RAG) Interview Questions and Answers
Baobao Algorithm Notes
Baobao Algorithm Notes
Sep 10, 2024 · Artificial Intelligence

Do LLMs Silence Human Voices? Unveiling the ‘Spiral of Silence’ in Retrieval‑Augmented Generation

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.

AI ImpactLLMOpen Domain QA
0 likes · 9 min read
Do LLMs Silence Human Voices? Unveiling the ‘Spiral of Silence’ in Retrieval‑Augmented Generation
DataFunSummit
DataFunSummit
Sep 5, 2024 · Artificial Intelligence

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

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.

NeMo FrameworkNvidiaRetrieval Augmented Generation
0 likes · 16 min read
NVIDIA’s End‑to‑End Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation
AI Large Model Application Practice
AI Large Model Application Practice
Sep 4, 2024 · Artificial Intelligence

When to Use GraphRAG vs. Traditional RAG and How to Combine Them

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.

GraphRAGLLMRAG
0 likes · 13 min read
When to Use GraphRAG vs. Traditional RAG and How to Combine Them
Baidu Geek Talk
Baidu Geek Talk
Sep 2, 2024 · Industry Insights

How a R&D Data Platform Leverages Large Language Models to Accelerate Issue Diagnosis

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.

Data PlatformRetrieval Augmented Generationgenerative BI
0 likes · 37 min read
How a R&D Data Platform Leverages Large Language Models to Accelerate Issue Diagnosis
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 19, 2024 · Artificial Intelligence

How Long‑Tail Knowledge Boosts Retrieval‑Augmented Large Language Models

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.

AI researchECE metricRetrieval Augmented Generation
0 likes · 9 min read
How Long‑Tail Knowledge Boosts Retrieval‑Augmented Large Language Models
DaTaobao Tech
DaTaobao Tech
Aug 12, 2024 · Artificial Intelligence

Challenges and Optimization Techniques for Retrieval‑Augmented Generation (RAG)

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.

AIRAGRetrieval Augmented Generation
0 likes · 22 min read
Challenges and Optimization Techniques for Retrieval‑Augmented Generation (RAG)
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 11, 2024 · Artificial Intelligence

Alibaba Cloud PAI’s Breakthroughs in Chinese Diffusion, Prompting, and LLM Knowledge Editing

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.

AI researchRetrieval Augmented Generationdiffusion models
0 likes · 11 min read
Alibaba Cloud PAI’s Breakthroughs in Chinese Diffusion, Prompting, and LLM Knowledge Editing
AntTech
AntTech
Aug 6, 2024 · Artificial Intelligence

Trustworthy Alignment of Retrieval‑Augmented Large Language Models via Reinforcement Learning

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.

ICML2024Retrieval Augmented Generationhallucination
0 likes · 5 min read
Trustworthy Alignment of Retrieval‑Augmented Large Language Models via Reinforcement Learning
JD Tech
JD Tech
Jul 22, 2024 · Artificial Intelligence

Task‑Aware Decoding (TaD): A Plug‑and‑Play Method to Mitigate Hallucinations in Large Language Models

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.

Fine-tuningLLMRetrieval Augmented Generation
0 likes · 18 min read
Task‑Aware Decoding (TaD): A Plug‑and‑Play Method to Mitigate Hallucinations in Large Language Models
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Jul 20, 2024 · Artificial Intelligence

Beyond RAG: How Mem0 Gives Large Language Models Super Memory for Personalized AI Apps

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.

AI agentsLLMMem0
0 likes · 9 min read
Beyond RAG: How Mem0 Gives Large Language Models Super Memory for Personalized AI Apps
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 27, 2024 · Artificial Intelligence

How to Supercharge Retrieval‑Augmented Generation: Papers, Techniques, and Real‑World Tips

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.

AI researchLLM optimizationRAG
0 likes · 22 min read
How to Supercharge Retrieval‑Augmented Generation: Papers, Techniques, and Real‑World Tips
JD Tech Talk
JD Tech Talk
Jun 20, 2024 · Artificial Intelligence

Applying Large Language Models to Courier Operations: Intelligent Operations, Q&A, Prompting, and Agents

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.

AI for logisticsIntelligent OperationsRetrieval Augmented Generation
0 likes · 14 min read
Applying Large Language Models to Courier Operations: Intelligent Operations, Q&A, Prompting, and Agents
Sohu Tech Products
Sohu Tech Products
Jun 5, 2024 · Artificial Intelligence

Retrieval Augmented Generation (RAG): Concepts, Workflow, and LangChain Implementation

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.

LLMLangChainPrompt engineering
0 likes · 15 min read
Retrieval Augmented Generation (RAG): Concepts, Workflow, and LangChain Implementation
DataFunSummit
DataFunSummit
May 16, 2024 · Artificial Intelligence

DataFun Data Science Summit: Cutting‑Edge Research on Causal Inference, Retrieval‑Augmented Generation, and LLM Content Detection

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.

AILLM detectionRetrieval Augmented Generation
0 likes · 14 min read
DataFun Data Science Summit: Cutting‑Edge Research on Causal Inference, Retrieval‑Augmented Generation, and LLM Content Detection
DataFunTalk
DataFunTalk
Mar 14, 2024 · Artificial Intelligence

Efficiency Challenges and Multi‑Layer Optimization for Large AI Models

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.

AI efficiencyMixture of ExpertsNVIDIA GTC
0 likes · 13 min read
Efficiency Challenges and Multi‑Layer Optimization for Large AI Models
Baidu Geek Talk
Baidu Geek Talk
Mar 13, 2024 · Artificial Intelligence

Understanding Retrieval-Augmented Generation (RAG) and Building a Personal Knowledge Base with ERNIE SDK and LangChain

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.

AIERNIE SDKKnowledge Base
0 likes · 13 min read
Understanding Retrieval-Augmented Generation (RAG) and Building a Personal Knowledge Base with ERNIE SDK and LangChain
DeWu Technology
DeWu Technology
Jan 22, 2024 · Artificial Intelligence

How to Integrate Business Systems with LLMs: Prompt, RAG, and Fine‑Tuning Strategies

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.

AI for businessFine-tuningKubeAI
0 likes · 20 min read
How to Integrate Business Systems with LLMs: Prompt, RAG, and Fine‑Tuning Strategies
Tencent Cloud Developer
Tencent Cloud Developer
Nov 8, 2023 · Artificial Intelligence

Comprehensive Overview of AI Agents: Concepts, Technical Frameworks, and Applications

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.

AI agentsAgent ArchitectureAutoGen
0 likes · 34 min read
Comprehensive Overview of AI Agents: Concepts, Technical Frameworks, and Applications
Baidu Tech Salon
Baidu Tech Salon
Oct 25, 2023 · Artificial Intelligence

Intelligent Question Answering Technology in Baidu Search: Development, Modeling, and Retrieval‑Enhanced Generation

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.

Baidu SearchRetrieval Augmented Generationknowledge distillation
0 likes · 18 min read
Intelligent Question Answering Technology in Baidu Search: Development, Modeling, and Retrieval‑Enhanced Generation
Baidu Geek Talk
Baidu Geek Talk
Oct 25, 2023 · Artificial Intelligence

How Baidu Search Is Transforming Machine Question Answering with Large‑Scale AI Models

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.

BaiduRetrieval Augmented Generationknowledge distillation
0 likes · 18 min read
How Baidu Search Is Transforming Machine Question Answering with Large‑Scale AI Models
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 19, 2023 · Artificial Intelligence

How to Build a Retrieval‑Augmented LLM Knowledge Base on Alibaba Cloud

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.

AILLMLangChain
0 likes · 37 min read
How to Build a Retrieval‑Augmented LLM Knowledge Base on Alibaba Cloud
dbaplus Community
dbaplus Community
Oct 14, 2023 · Artificial Intelligence

Demystifying Retrieval‑Augmented Generation: From Theory to Working Chatbot

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.

ChatbotEmbeddingLLM
0 likes · 19 min read
Demystifying Retrieval‑Augmented Generation: From Theory to Working Chatbot
DataFunSummit
DataFunSummit
Sep 19, 2023 · Artificial Intelligence

Advances in Information Extraction: From PLM to LLM Paradigms at Alibaba DAMO Academy

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.

Alibaba DAMOFew‑Shot LearningRetrieval Augmented Generation
0 likes · 24 min read
Advances in Information Extraction: From PLM to LLM Paradigms at Alibaba DAMO Academy
phodal
phodal
Sep 17, 2023 · Artificial Intelligence

How Chocolate Factory’s Codebase AI Assistant Boosts Code Search with RAG

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.

AI AssistantKotlinRetrieval Augmented Generation
0 likes · 11 min read
How Chocolate Factory’s Codebase AI Assistant Boosts Code Search with RAG