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retrieval augmentation

20 articles · Page 1 of 1
DataFunSummit
DataFunSummit
Jun 23, 2026 · Artificial Intelligence

Financial Large Language Models: Architecture Shifts, Engineering Lessons, and Cutting‑Edge Agent Strategies

The article analyzes how strict compliance, data‑security, and rigorous business requirements reshape financial large‑model deployments, detailing a PageIndex‑based retrieval architecture, engineering pitfalls such as rule explosion and prompt bloat, model‑selection trade‑offs, and forward‑looking agent‑centric designs.

Large Language ModelsPrompt Engineeringagentic AI
0 likes · 11 min read
Financial Large Language Models: Architecture Shifts, Engineering Lessons, and Cutting‑Edge Agent Strategies
AntData
AntData
Jun 12, 2026 · Artificial Intelligence

Rethinking AI Memory: From Raw Ledger to Policy‑Driven Closed Loop

The article argues that AI memory is not mere storage but an external state that feeds decisions, proposes three core propositions—Memory as decision‑usable external state, a minimal closure of Raw Ledger + Views + Policy, and event sequences as the fundamental unit—and details how a System 1 + System 2 architecture, non‑parametric designs, temporal handling, and learnable policies together shape the practical limits of modern agentic memory systems.

AI memoryagent architecturenon‑parametric memory
0 likes · 42 min read
Rethinking AI Memory: From Raw Ledger to Policy‑Driven Closed Loop
Architecture and Beyond
Architecture and Beyond
Jun 7, 2026 · Artificial Intelligence

From Fragmented Retrieval to Deep Reasoning: Reshaping AI Agent Knowledge Engines

The article analyzes why traditional RAG fails on complex, multi‑step enterprise queries, explains how GraphRAG introduces explicit entity‑relationship graphs to enable multi‑hop navigation, explainability, and temporal reasoning, and outlines practical architectures, lightweight and dynamic graph strategies, and trade‑offs for real‑world deployment.

AI agentsGraphRAGentity alignment
0 likes · 26 min read
From Fragmented Retrieval to Deep Reasoning: Reshaping AI Agent Knowledge Engines
Huolala Tech
Huolala Tech
Apr 29, 2026 · Artificial Intelligence

From MVP to 1.0: A Practical Roadmap for AI‑Powered Test Case Generation

The article analyses the structural bottlenecks of manual test case creation, validates an MVP that keeps human testing logic while automating repetitive steps, identifies three core limitations of the MVP, and then details a 1.0 upgrade that adds multimodal input parsing, prompt engineering, knowledge‑graph RAG and retrieval loops, culminating in measurable productivity gains and a reusable framework for AI‑driven testing.

AI testingMVPPrompt Engineering
0 likes · 17 min read
From MVP to 1.0: A Practical Roadmap for AI‑Powered Test Case Generation
AI Explorer
AI Explorer
Mar 15, 2026 · Artificial Intelligence

How OpenViking Redesigns AI Agent Memory with a File‑System Approach

OpenViking, an open‑source project from ByteDance, introduces a file‑system‑style context database for AI agents that unifies memory, resources, and skills, offers hierarchical L0‑L2 loading, visualizable retrieval paths, and self‑evolution, aiming to eliminate fragmented context management and improve debugging, cost, and scalability.

AI AgentObservabilityOpenViking
0 likes · 8 min read
How OpenViking Redesigns AI Agent Memory with a File‑System Approach
PaperAgent
PaperAgent
Jan 27, 2026 · Artificial Intelligence

How Agentic‑R Boosts Multi‑Turn Retrieval for LLMs by 2–3 EM Points

This article analyzes the Agentic‑R framework, which upgrades traditional single‑hop Retrieval‑Augmented Generation by introducing dual‑perspective scoring and a bidirectional flywheel, resulting in 2–3 absolute EM improvements across seven QA datasets and a 10–15% reduction in search rounds.

Agentic SearchLLMRAG
0 likes · 6 min read
How Agentic‑R Boosts Multi‑Turn Retrieval for LLMs by 2–3 EM Points
Data Party THU
Data Party THU
Dec 29, 2025 · Artificial Intelligence

Unlocking AI Agent Memory: A Deep Dive into Forms, Functions, and Dynamics

This article reviews the survey "Memory in the Age of AI Agents," presenting a comprehensive taxonomy that classifies agent memory by its forms, functions, and dynamic mechanisms, and explores future directions such as generative memory, reinforcement‑learning‑driven management, multimodal storage, and trustworthy handling.

AI agentsFuture AILLM
0 likes · 14 min read
Unlocking AI Agent Memory: A Deep Dive into Forms, Functions, and Dynamics
AI Architecture Hub
AI Architecture Hub
Dec 27, 2025 · Artificial Intelligence

How GraphRAG Turns Knowledge Graphs into Smarter Retrieval for LLMs

GraphRAG extends traditional Retrieval‑Augmented Generation by building a knowledge graph from documents, extracting entities and relationships, performing community detection, and supporting both local and global searches, offering detailed step‑by‑step guidance, code examples, configuration tips, and a comparison with classic RAG approaches.

GraphRAGLLMNeo4j
0 likes · 28 min read
How GraphRAG Turns Knowledge Graphs into Smarter Retrieval for LLMs
dbaplus Community
dbaplus Community
May 31, 2025 · Artificial Intelligence

How RAG is Shaping the Future of AI-Powered User Experience

Amid the rapid rise of large language models, this article examines RAG’s development, technical hurdles, core strategies, and future outlook, illustrating how Alibaba’s Chatbot and Copilot projects boost retrieval accuracy to 90% and generation precision to 85% while tackling data quality, heterogeneous retrieval, and evaluation challenges.

AI SearchEvaluation MetricsRAG
0 likes · 27 min read
How RAG is Shaping the Future of AI-Powered User Experience
Baidu Tech Salon
Baidu Tech Salon
Nov 14, 2024 · Artificial Intelligence

How Baidu’s Wenxin Model Hit 430 Million Users and What Its New Tech Means for AI

At Baidu World 2024, CTO Wang Haifeng revealed that Wenxin Yiyan has reached 430 million users, detailed the model’s retrieval‑augmented and multimodal generation breakthroughs, showcased intelligent‑agent‑driven coding tools, and highlighted expanding AI applications across education, sports, and industry.

AIIntelligent agentsMultimodal Generation
0 likes · 7 min read
How Baidu’s Wenxin Model Hit 430 Million Users and What Its New Tech Means for AI
DevOps
DevOps
Sep 13, 2024 · Artificial Intelligence

15 Advanced Retrieval‑Augmented Generation (RAG) Techniques for Production‑Ready AI Solutions

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.

AI productionLLMRAG
0 likes · 8 min read
15 Advanced Retrieval‑Augmented Generation (RAG) Techniques for Production‑Ready AI Solutions
Baidu Tech Salon
Baidu Tech Salon
Jun 24, 2024 · Artificial Intelligence

Paperpolisher: AI-Powered Academic Paper Translation and Polishing Assistant

Paperpolisher is an AI-powered tool using Baidu's ERNIE large model and Comate to translate and polish Chinese academic papers into high-quality English, leveraging large paper datasets and retrieval augmentation, streamlining code generation and improving acceptance chances for submissions to top conferences.

AI coding assistantBaidu ComateERNIE large model
0 likes · 9 min read
Paperpolisher: AI-Powered Academic Paper Translation and Polishing Assistant
Baobao Algorithm Notes
Baobao Algorithm Notes
Jun 3, 2024 · Artificial Intelligence

Can Adversarial Training Make Retrieval‑Augmented Generators More Robust?

Recent arXiv work introduces ATM, an adversarially‑tuned multi‑agent system that iteratively pits a fake‑knowledge attacker against a generator, dramatically improving retrieval‑augmented language models’ resistance to hallucinated content and boosting performance on knowledge‑intensive benchmarks, even with noisy or irrelevant documents.

Large Language ModelsRAGadversarial training
0 likes · 12 min read
Can Adversarial Training Make Retrieval‑Augmented Generators More Robust?
Cloud Native Technology Community
Cloud Native Technology Community
Feb 8, 2024 · Artificial Intelligence

How Retrieval‑Augmented Generation Boosts LLM Accuracy and Trust

Retrieval‑augmented generation (RAG) enhances large language models by fetching up‑to‑date, authoritative information from external sources, addressing hallucinations, outdated knowledge, and lack of citations, while offering cost‑effective implementation, improved relevance, user trust, and greater developer control through vector databases, semantic search, and prompt engineering.

AILarge Language ModelsPrompt Engineering
0 likes · 10 min read
How Retrieval‑Augmented Generation Boosts LLM Accuracy and Trust
Alimama Tech
Alimama Tech
Oct 18, 2023 · Artificial Intelligence

Technical Challenges and Directions for Large‑Model Applications in E‑commerce

Taobao Group’s ten large‑model challenges target e‑commerce AI by demanding domain‑specific pre‑training, multi‑step reasoning, extended context handling, factual reliability, intelligent tool orchestration, robust retrieval integration, fuzzy‑intent tool selection, scalable multi‑objective RLHF, improved query rewriting, and knowledge‑driven recommendation.

Large Language ModelsRLHFe-commerce
0 likes · 16 min read
Technical Challenges and Directions for Large‑Model Applications in E‑commerce
DaTaobao Tech
DaTaobao Tech
Oct 18, 2023 · Artificial Intelligence

Large Model Application Challenges for E-commerce

Taobao Group’s ten large‑model e‑commerce challenges call for researchers to build domain‑specific data pipelines, mitigate forgetting, balance expertise with generality, enable multi‑step reasoning, handle long contexts, reduce hallucinations, integrate tool use, improve fuzzy intent detection, apply multi‑objective RLHF, and generate cognitively novel recommendations.

Large Language ModelsRLHFknowledge hallucination
0 likes · 14 min read
Large Model Application Challenges for E-commerce
DataFunSummit
DataFunSummit
Sep 23, 2023 · Artificial Intelligence

Personalized Large Models: Technical Practice, Challenges, and Future Directions

This article presents a comprehensive overview of personalized large models, covering their definition, four‑fold capabilities (knowledge, personality, emotion, memory), practical applications, challenges such as knowledge hallucination, retrieval‑augmented solutions, and detailed discussions on persona consistency and controllable language style.

AI dialogue systemsknowledge hallucinationlanguage style control
0 likes · 13 min read
Personalized Large Models: Technical Practice, Challenges, and Future Directions
Baidu Geek Talk
Baidu Geek Talk
May 8, 2023 · Artificial Intelligence

Augmented Language Models: Reasoning and External Tool Utilization

The survey shows that once language models exceed roughly ten billion parameters they spontaneously acquire two complementary abilities—step‑by‑step reasoning, often elicited by chain‑of‑thought prompts or scratch‑pad training, and the capacity to invoke external tools such as search engines, calculators, or robots—enabling them to retrieve up‑to‑date information, perform complex computations, and act in the world, thereby advancing toward general artificial intelligence.

AIPrompt EngineeringTool Use
0 likes · 20 min read
Augmented Language Models: Reasoning and External Tool Utilization
DataFunSummit
DataFunSummit
Apr 20, 2023 · Artificial Intelligence

Mengzi Lightweight Model Technology System and Advances in Small‑Scale and Retrieval‑Augmented Pretraining

This presentation introduces the Mengzi lightweight model technology stack, covering large‑scale pre‑training, motivations for lightweight models, detailed techniques such as knowledge and sequence‑relation enhancement, training optimization, model compression, retrieval‑augmented pre‑training, multimodal extensions, open‑source releases, and real‑world applications.

Knowledge DistillationLarge Language ModelsMultimodal
0 likes · 23 min read
Mengzi Lightweight Model Technology System and Advances in Small‑Scale and Retrieval‑Augmented Pretraining