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iKang Technology Team

Feb 7, 2025 · Artificial Intelligence

Retrieval‑Augmented Generation (RAG) with LangChain: Concepts and Python Implementation

Retrieval‑Augmented Generation (RAG) using LangChain lets developers enhance large language models by embedding user queries, fetching relevant documents from a vector store, inserting the context into a prompt template, and generating concise, source‑grounded answers, offering low‑cost, up‑to‑date knowledge while reducing hallucinations and fine‑tuning expenses.

LLMLangChainPython

0 likes · 10 min read

Retrieval‑Augmented Generation (RAG) with LangChain: Concepts and Python Implementation

HelloTech

Apr 10, 2024 · Artificial Intelligence

An Overview of LangChain: Architecture, Core Components, and Code Examples

LangChain is an open‑source framework that provides Python and JavaScript SDKs, templates, and services such as LangServe and LangSmith to compose models, embeddings, prompts, indexes, memory, chains, and agents via a concise expression language, enabling rapid prototyping, debugging, and deployment of LLM‑driven applications.

AI EngineeringAgentsJavaScript

0 likes · 19 min read

An Overview of LangChain: Architecture, Core Components, and Code Examples

Bilibili Tech

Jun 7, 2024 · Artificial Intelligence

AI Development for Frontend Developers: From Basics to Agent Implementation

This article guides frontend developers through AI development, comparing model training, fine‑tuning, prompt engineering, and Retrieval‑Augmented Generation, then explains agent creation via ReAct and tool‑call methods, and showcases Langchain and Flowise as low‑code frameworks for building domain‑specific AI agents.

AI DevelopmentAgentFlowise

0 likes · 13 min read

AI Development for Frontend Developers: From Basics to Agent Implementation

Tencent Cloud Developer

Sep 27, 2024 · Artificial Intelligence

A Comprehensive Prompt Engineering Framework: Universal Templates, RAG, Few‑Shot, Memory, and Automated Optimization

The article presents a universal four‑part prompt template—role, problem description, goal, and requirements—augmented with role definitions, RAG‑based knowledge retrieval, few‑shot examples, memory handling, temperature/top‑p tuning, and automated optimization techniques such as APE, APO, and OPRO, enabling developers to reliably craft high‑quality prompts for LLMs.

AI Prompt OptimizationPrompt EngineeringRAG

0 likes · 26 min read

A Comprehensive Prompt Engineering Framework: Universal Templates, RAG, Few‑Shot, Memory, and Automated Optimization

JD Tech

May 31, 2024 · Artificial Intelligence

Understanding Large Language Models, Retrieval‑Augmented Generation, and AI Agents: Concepts, Engineering Practices, and Applications

This article explains the fundamentals and engineering practices of large language models (LLM), retrieval‑augmented generation (RAG) and AI agents, compares small and large embedding models, provides Python code for vector‑database RAG with Chroma, and discusses integration, use cases, and future challenges in AI development.

AI EngineeringAI agentsLLM

0 likes · 41 min read

Understanding Large Language Models, Retrieval‑Augmented Generation, and AI Agents: Concepts, Engineering Practices, and Applications

JD Tech Talk

Jul 16, 2024 · Artificial Intelligence

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

TaD, a task‑aware decoding technique jointly developed by JD.com and Tsinghua University and presented at IJCAI 2024, leverages differences between pre‑ and post‑fine‑tuned LLM outputs to construct knowledge vectors, significantly reducing hallucinations across various models, tasks, and data‑scarce scenarios, especially when combined with RAG.

AILLMRAG

0 likes · 18 min read

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

Rare Earth Juejin Tech Community

Apr 29, 2024 · Artificial Intelligence

Building Enterprise‑Grade Retrieval‑Augmented Generation (RAG) Systems: Challenges, Fault Points, and Best Practices

This comprehensive guide explores the complexities of building enterprise‑level Retrieval‑Augmented Generation (RAG) systems, detailing common failure points, architectural components such as authentication, input guards, query rewriting, document ingestion, indexing, storage, retrieval, generation, observability, caching, and multi‑tenant considerations, and provides actionable best‑practice recommendations for developers and technical leaders.

LLMRAGcaching

0 likes · 32 min read

Building Enterprise‑Grade Retrieval‑Augmented Generation (RAG) Systems: Challenges, Fault Points, and Best Practices

Rare Earth Juejin Tech Community

Mar 30, 2024 · Artificial Intelligence

Comprehensive Guide to Coze: AI Bot Development, Prompt Engineering, and Workflow Design

This article provides an in‑depth overview of the Coze low‑code AI bot platform, covering its core features, product comparisons, step‑by‑step bot creation, RAG implementation, plugin usage, memory mechanisms, cron jobs, agent design, advanced workflow techniques, quality management, and future prospects.

AI BotCozeLLM

0 likes · 25 min read

Comprehensive Guide to Coze: AI Bot Development, Prompt Engineering, and Workflow Design

Architect

Mar 15, 2025 · Artificial Intelligence

Why Building Your Own RAG System Is a Costly Mistake

The article explains that developing a custom Retrieval‑Augmented Generation (RAG) solution incurs hidden infrastructure, personnel, and security costs, leads to operational overload and budget overruns, and is rarely justified compared to purchasing a proven vendor solution.

AILLMRAG

0 likes · 11 min read

Why Building Your Own RAG System Is a Costly Mistake

Architect

Aug 2, 2024 · Artificial Intelligence

Building AI‑Native Applications with Spring AI: A Complete Tutorial

This article explains how to quickly develop an AI‑native application using Spring AI, covering core features such as chat models, prompt templates, function calling, structured output, image generation, embedding, vector stores, and Retrieval‑Augmented Generation (RAG), and provides end‑to‑end Java code examples for building a simple AI‑driven service.

AI nativeJavaPrompt Engineering

0 likes · 40 min read

Building AI‑Native Applications with Spring AI: A Complete Tutorial

DataFunSummit

Jan 26, 2025 · Artificial Intelligence

ChatBI in Automotive Enterprises: Challenges, Architecture, and Implementation

This article examines the rise of ChatBI in automotive companies, outlining current BI challenges, the five “no” and five “difficulties” issues, the motivations for adopting ChatBI, its evolving architecture, and practical implementation steps to achieve data‑driven decision making.

AIAutomotiveChatBI

0 likes · 17 min read

ChatBI in Automotive Enterprises: Challenges, Architecture, and Implementation

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.

ChunkingHybrid SearchRAG

0 likes · 23 min read

RAG2.0 Engine Design Challenges and Implementation

DataFunSummit

Jan 11, 2025 · Artificial Intelligence

Generative AI Applications, MLOps, and LLMOps: A Comprehensive Overview

This article presents a detailed overview of generative AI lifecycle management, covering practical use cases such as email summarization, the roles of providers, fine‑tuners and consumers, MLOps/LLMOps processes, retrieval‑augmented generation, efficient fine‑tuning methods like PEFT, and Amazon Bedrock services for model deployment and monitoring.

Amazon BedrockLLMOpsPEFT

0 likes · 14 min read

Generative AI Applications, MLOps, and LLMOps: A Comprehensive Overview

DataFunSummit

Oct 27, 2024 · Artificial Intelligence

How Siemens Harnesses Generative AI to Build the Enterprise Knowledge Chatbot “XiaoYu”

This article describes Siemens' journey in applying generative AI and Retrieval‑Augmented Generation to create an internal knowledge chatbot, detailing the business challenges, technical architecture, data integration, multi‑modal capabilities, deployment outcomes, and strategic lessons for enterprise AI adoption.

AI chatbotEnterprise Knowledge ManagementRAG

0 likes · 21 min read

How Siemens Harnesses Generative AI to Build the Enterprise Knowledge Chatbot “XiaoYu”

DataFunSummit

Aug 29, 2024 · Artificial Intelligence

Intelligent NPC Practices in Tencent Games: Multi‑Modal LLM Solutions and System Optimizations

This article details Tencent Game's end‑to‑end approach to building intelligent NPCs, covering the opportunities brought by AI, the practical implementation of multimodal LLM‑driven dialogue, knowledge‑augmented retrieval, long‑context handling, safety measures, multimodal expression (voice and facial animation), and system‑level performance optimizations for real‑time deployment.

AILLMNPC

0 likes · 18 min read

Intelligent NPC Practices in Tencent Games: Multi‑Modal LLM Solutions and System Optimizations

DataFunSummit

May 10, 2024 · Artificial Intelligence

LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions

This article introduces LLMOps by defining large language model operations, explains the three stages of LLM development, details modern fine‑tuning methods such as PEFT, Adapter, Prefix, Prompt and LoRA, outlines the architecture for building LLM applications, discusses the main difficulties of agent‑based deployments, and presents practical solutions including Prompt IDE, low‑code deployment, monitoring and cost control.

AI operationsFine‑tuningLLMOps

0 likes · 14 min read

LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions

DataFunTalk

Apr 29, 2024 · Artificial Intelligence

Practical Experience and Q&A Exploration of Patent Large Models

This article presents a comprehensive overview of the development, training, data preparation, algorithmic strategies, evaluation methods, and RAG integration for a domain‑specific patent large language model, highlighting challenges, practical results, and future research directions.

Domain-specific ModelEvaluationPatent AI

0 likes · 19 min read

Practical Experience and Q&A Exploration of Patent Large Models

DataFunTalk

Mar 15, 2024 · Artificial Intelligence

NVIDIA’s NeMo Framework and TensorRT‑LLM: Full‑Stack Solutions for Large Language Models and Retrieval‑Augmented Generation

This article explains NVIDIA’s end‑to‑end ecosystem for large language models, covering the NeMo Framework’s data processing, distributed training, model fine‑tuning, inference acceleration with TensorRT‑LLM, deployment via Triton, and Retrieval‑Augmented Generation (RAG) techniques that enhance model reliability and performance.

AINVIDIANeMo

0 likes · 16 min read

NVIDIA’s NeMo Framework and TensorRT‑LLM: Full‑Stack Solutions for Large Language Models and Retrieval‑Augmented Generation

DataFunTalk

Nov 17, 2023 · Databases

Cost as the Primary Driver of Vector Database Industry Development

Vector databases gain traction because they dramatically reduce storage, learning, scaling, and large‑model limitations costs by enabling semantic similarity search, RAG‑based prompt optimization, efficient high‑dimensional indexing, and cloud‑native architectures, making them essential for modern AI applications despite the promotional context.

AIRAGVector Database

0 likes · 11 min read

Cost as the Primary Driver of Vector Database Industry Development

Model Perspective

Jul 30, 2024 · Artificial Intelligence

Your Complete AI Learning Roadmap: From Basics to Large Model Mastery

This guide presents a comprehensive AI learning roadmap, dividing study into five progressive stages—from foundational math and programming to core deep‑learning and reinforcement‑learning techniques, large‑model training, industry applications, and future trends—plus curated book lists, tool recommendations, and practical RAG tutorials.

AI learning roadmapAI resourcesDeep Learning

0 likes · 9 min read

Your Complete AI Learning Roadmap: From Basics to Large Model Mastery