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RAG

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Tencent Cloud Developer
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
Tencent Cloud Developer
Tencent Cloud Developer
Mar 4, 2025 · Artificial Intelligence

A Practical Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling and AI Agents

The guide teaches non‑AI developers how to build practical LLM‑powered applications by mastering prompt engineering, function calling, retrieval‑augmented generation, and AI agents, and introduces the Modal Context Protocol for seamless tool integration, offering a clear learning path to leverage large language models without deep theory.

AI AgentLLMPrompt Engineering
0 likes · 48 min read
A Practical Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling and AI Agents
iQIYI Technical Product Team
iQIYI Technical Product Team
Dec 19, 2024 · Artificial Intelligence

Project BaixiaoSheng: An AI‑Powered Project Management Assistant – iQIYI Case Study

Project BaixiaoSheng, iQIYI’s AI‑powered project management assistant unveiled at the 13th TOP 100 Global Software Case Study Summit, uses a Retrieval‑Augmented Generation framework with static knowledge Q&A, dynamic data consulting, and scenario‑assistant automation to cut context‑switching, streamline data flow, and boost cross‑system efficiency, while future plans target fine‑tuned LLMs, multi‑model fusion, and AI‑agent orchestration.

AIProject ManagementRAG
0 likes · 11 min read
Project BaixiaoSheng: An AI‑Powered Project Management Assistant – iQIYI Case Study
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 26, 2024 · Artificial Intelligence

AI-Powered Search in iQIYI: Techniques, Architecture, and Implementation

iQIYI’s AI‑powered search expands beyond title‑only queries by handling fuzzy role, plot, star, award, and semantic searches, using Chain‑of‑Thought‑generated TIPS, Retrieval‑Augmented Generation with sophisticated indexing, chunking, embedding, reranking, and prompt‑engineering to deliver personalized, accurate video recommendations that boost user engagement.

AI SearchChain-of-ThoughtQuery Guidance
0 likes · 15 min read
AI-Powered Search in iQIYI: Techniques, Architecture, and Implementation
37 Interactive Technology Team
37 Interactive Technology Team
May 27, 2024 · Artificial Intelligence

Enhancing AI Code Review Quality with Contextual Embedding and Function Calling

The article explains how AI code reviews suffer from missing context, and improves them by embedding the codebase, using Retrieval‑Augmented Generation to fetch relevant snippets, and adding a function‑calling tool that lets the model autonomously request additional code, resulting in precise, bug‑detecting feedback.

AI code reviewLLMRAG
0 likes · 8 min read
Enhancing AI Code Review Quality with Contextual Embedding and Function Calling
Tencent Cloud Developer
Tencent Cloud Developer
Jul 30, 2024 · Artificial Intelligence

A Systematic Guide to Prompt Engineering: From Zero to One

This guide walks readers from beginner to proficient Prompt Engineer by outlining the evolution of prompting, introducing a universal four‑component template, and detailing a five‑step workflow—including refinement, retrieval‑augmented generation, chain‑of‑thought reasoning, and advanced tuning techniques—plus evaluation metrics for LLM performance.

AI promptingChain-of-ThoughtLLM optimization
0 likes · 51 min read
A Systematic Guide to Prompt Engineering: From Zero to One
Tencent Cloud Developer
Tencent Cloud Developer
Jun 5, 2024 · Artificial Intelligence

Introduction to AI Development and Practical Applications

The article surveys AI development from early GPT experiments to real‑world deployments, explaining how tools like LangChain and Retrieval‑Augmented Generation enable sophisticated agents, multi‑prompt workflows, and function calls for chatbots, education, and creative content while addressing accuracy, resource, and ethical challenges.

AI DemosAI DevelopmentBig Models
0 likes · 34 min read
Introduction to AI Development and Practical Applications
Youzan Coder
Youzan Coder
May 8, 2025 · Artificial Intelligence

Building and Optimizing a Store Smart Assistant with Aily: Architecture, Workflow, and Practical Lessons

The article details how Youzan’s Store Smart Assistant was built on the Feishu Aily platform, describing why Aily was chosen, the three‑stage development process, deep system integration, practical tips for knowledge‑base management and model stability, and the resulting efficiency gains such as handling 80% of routine queries.

AI AssistantAily platformLLM
0 likes · 24 min read
Building and Optimizing a Store Smart Assistant with Aily: Architecture, Workflow, and Practical Lessons
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
Baidu Geek Talk
Baidu Geek Talk
Oct 28, 2024 · Artificial Intelligence

Baidu Intelligent Cloud Qianfan AppBuilder: Enterprise-Level Large Model Application Development Platform

Baidu Intelligent Cloud’s Qianfan AppBuilder 3.0 offers an enterprise‑grade platform that simplifies large‑model application development by providing high‑accuracy RAG, robust agent scheduling, extensive integration, secure private‑or‑hybrid deployment, and a guided methodology, enabling industries to transform processes, add AI copilots, and create novel capabilities.

AI integrationAgent developmentBaidu Intelligent Cloud
0 likes · 12 min read
Baidu Intelligent Cloud Qianfan AppBuilder: Enterprise-Level Large Model Application Development Platform
Baidu Tech Salon
Baidu Tech Salon
Nov 11, 2024 · Cloud Native

Baidu Cloud Native Data Platform: Empowering Enterprise AI in the LLM Era

To empower enterprise AI in the LLM era, Baidu Cloud unveils a cloud‑native data platform featuring upgraded databases—PegaDB, GaiaDB 5.0, Vector DB 2.0, Palo 2.0—and integrated services like DBSC 2.0, EDAP 2.0, and DBStack, delivering high‑performance, cost‑effective handling of structured, unstructured, and vector data for fine‑tuning and Enterprise RAG.

DBStackData LakehouseEDAP
0 likes · 10 min read
Baidu Cloud Native Data Platform: Empowering Enterprise AI in the LLM Era
Baidu Tech Salon
Baidu Tech Salon
May 10, 2024 · Artificial Intelligence

Baidu Comate: Core Capabilities of Intelligent Code Assistant

The article surveys Baidu Comate, an AI‑powered code assistant built on the Wenxin (ERNIE) large model, tracing software development from the 1950s crisis through the internet and open‑source era to today’s AI‑driven tools, and highlights its features and demonstration at a global development conference.

AI codingBaidu ComateIDE Plugin
0 likes · 7 min read
Baidu Comate: Core Capabilities of Intelligent Code Assistant
Sohu Tech Products
Sohu Tech Products
Nov 27, 2024 · Artificial Intelligence

RAG Technology and Practical Application in Multi-Modal Query: Using Chinese-CLIP and Redis Search

The article explains how Retrieval‑Augmented Generation (RAG) outperforms direct LLM inference by enabling real‑time knowledge updates and lower costs, and demonstrates a practical multi‑modal RAG pipeline that uses Chinese‑CLIP for vector encoding, various chunking strategies, and Redis Search for fast vector storage and retrieval.

Chinese CLIPChunkingLLM
0 likes · 17 min read
RAG Technology and Practical Application in Multi-Modal Query: Using Chinese-CLIP and Redis Search
Sohu Tech Products
Sohu Tech Products
Nov 6, 2024 · Artificial Intelligence

RAG2.0 Engine Design Challenges and Implementation

The talk outlines RAG2.0’s design challenges—low vector recall, complex documents, semantic gaps—and presents a two‑stage architecture using deep multimodal understanding and knowledge‑graph‑enhanced retrieval, detailing advanced chunking, multi‑index and multi‑path retrieval, efficient sorting models like ColBERT, and future multi‑modal and memory‑augmented agent directions.

ColBERTDelayed InteractionDocument Understanding
0 likes · 23 min read
RAG2.0 Engine Design Challenges and Implementation
Sohu Tech Products
Sohu Tech Products
Mar 27, 2024 · Artificial Intelligence

NVIDIA NeMo Framework, TensorRT‑LLM, and RAG for Large Language Model Solutions

NVIDIA’s comprehensive LLM ecosystem combines the full‑stack NeMo Framework for data curation, distributed training, fine‑tuning, inference acceleration with TensorRT‑LLM and Triton, plus Retrieval‑Augmented Generation and Guardrails, enabling efficient, low‑latency, knowledge‑grounded model deployment across clusters.

AI accelerationNVIDIANeMo Framework
0 likes · 16 min read
NVIDIA NeMo Framework, TensorRT‑LLM, and RAG for Large Language Model Solutions
Sohu Tech Products
Sohu Tech Products
Mar 27, 2024 · Artificial Intelligence

Building a RAG Application with Baidu Vector Database and Qianfan Embedding

This tutorial walks through building a Retrieval‑Augmented Generation application by setting up Baidu’s Vector Database and Qianfan embedding service, configuring credentials, creating a document database and vector table, loading and chunking PDFs, generating embeddings, storing them, and performing scalar, vector and hybrid similarity searches, ready for integration with Wenxin LLM for answer generation.

AI applicationsBaidu QianfanHNSW
0 likes · 11 min read
Building a RAG Application with Baidu Vector Database and Qianfan Embedding
JD Retail Technology
JD Retail Technology
Feb 18, 2025 · Artificial Intelligence

Engineering Practices of JD Advertising Agent: JDZunTong Intelligent Assistant

JD’s advertising R&D team created the JDZunTong Intelligent Assistant by engineering a modular Agent platform that combines advanced Retrieval‑Augmented Generation (RAG 1.0 → 2.0) and Function‑Call capabilities, a visual designer, custom tool registration, and a native Python workflow engine to deliver intelligent customer service, data queries, and ad creation for merchants.

AIFunction CallJD Advertising
0 likes · 18 min read
Engineering Practices of JD Advertising Agent: JDZunTong Intelligent Assistant
JD Tech Talk
JD Tech Talk
Jan 9, 2025 · Artificial Intelligence

Practical Guide to Building Retrieval‑Augmented Generation (RAG) Applications with LangChain4j in Java

This article provides a step‑by‑step tutorial for Java engineers on using the LangChain4j framework to implement Retrieval‑Augmented Generation (RAG) with large language models, covering concepts, environment setup, code integration, document splitting, embedding, vector‑store operations, and prompt engineering.

LangChain4jRAGembedding
0 likes · 35 min read
Practical Guide to Building Retrieval‑Augmented Generation (RAG) Applications with LangChain4j in Java