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2583 articles · Page 23 of 26
Data Thinking Notes
Data Thinking Notes
Sep 1, 2024 · Artificial Intelligence

Master LLMs: Basics, Prompt Engineering, RAG, Agents & Multimodal AI

This article provides a comprehensive overview of large language models, covering their fundamental concepts, historical milestones, parameter scaling, prompt engineering techniques, retrieval‑augmented generation, autonomous agents, and multimodal model applications, illustrating how these technologies reshape AI capabilities across domains.

AI AgentsLLMPrompt Engineering
0 likes · 22 min read
Master LLMs: Basics, Prompt Engineering, RAG, Agents & Multimodal AI
DataFunSummit
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.

AILLMMultimodal
0 likes · 18 min read
Intelligent NPC Practices in Tencent Games: Multi‑Modal LLM Solutions and System Optimizations
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 27, 2024 · Industry Insights

What Real‑World LLM Researchers Face: Scaling Limits, Data Bottlenecks, and Deployment Challenges

The author shares a candid account of recent large‑model experiments, highlighting why most labs struggle to exceed 100 B parameters, how data and hardware constraints shape model iteration, and the practical engineering, safety, and multimodal challenges that dictate real‑world LLM deployment.

AI industryAI scalingHardware Acceleration
0 likes · 6 min read
What Real‑World LLM Researchers Face: Scaling Limits, Data Bottlenecks, and Deployment Challenges
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 22, 2024 · Industry Insights

How LLMs Turned Into a Powerful Productivity Boost for Developers

The author reflects on two years of LLM work, describing how large language models shattered old NLP expertise, reshaped daily engineering tasks, leveled the playing field for newcomers, sparked a golden era of AI productivity, and why concerns about a bubble are largely irrelevant for programmers.

Artificial IntelligenceLLMProductivity
0 likes · 10 min read
How LLMs Turned Into a Powerful Productivity Boost for Developers
21CTO
21CTO
Aug 19, 2024 · Artificial Intelligence

How to Become an AI Agent Developer: A Step‑by‑Step Roadmap

This article explains what AI agents are, why they matter, the essential soft and hard skills needed, and provides a detailed step‑by‑step roadmap for anyone aspiring to become an AI agent developer in the emerging AI‑driven software landscape.

AI AgentLLMbackend
0 likes · 6 min read
How to Become an AI Agent Developer: A Step‑by‑Step Roadmap
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 19, 2024 · Artificial Intelligence

Ensuring Stable AI Agents: Engineering Practices, RAG, and Monitoring

This article shares engineering insights from Hema’s AI smart customer service deployment, detailing key stability factors for AI agents—including hallucination mitigation, memory integration, RAG enhancement, exception handling, and comprehensive monitoring—to improve reliability and performance in real‑world e‑commerce chatbot scenarios.

AI AgentE‑commerceLLM
0 likes · 13 min read
Ensuring Stable AI Agents: Engineering Practices, RAG, and Monitoring
JavaEdge
JavaEdge
Aug 17, 2024 · Artificial Intelligence

Exploring LangGraph Studio: A Visual IDE for Building LLM Agents

LangGraph Studio is a new visual IDE that simplifies the development, debugging, and interactive iteration of complex LLM‑based agent applications, offering features such as graph visualization, real‑time state inspection, code‑aware debugging, and seamless integration with LangSmith, with step‑by‑step guidance for desktop users.

AI developmentAgent IDELLM
0 likes · 8 min read
Exploring LangGraph Studio: A Visual IDE for Building LLM Agents
21CTO
21CTO
Aug 17, 2024 · Artificial Intelligence

Understanding Large Language Models: Training, Uses, and a Llama 3 Code Demo

This article explains what large language models (LLMs) are, how they are trained, their diverse applications across industries, the challenges they face, and provides a practical Python example using Replicate to run Meta's Llama 3‑70b‑instruct model.

AILLMMachine Learning
0 likes · 11 min read
Understanding Large Language Models: Training, Uses, and a Llama 3 Code Demo
Meituan Technology Team
Meituan Technology Team
Aug 15, 2024 · Artificial Intelligence

Meituan's Exploration and Practice in Advertising Algorithm: Information Flow Ad Estimation

This article details Meituan Waimai's feed advertising system, covering business characteristics, the evolution of estimation models, and practical implementations such as decision‑path modeling, ultra‑long/wide user modeling, full‑reconstruction techniques, and the integration of large language models for CTR prediction.

CTR estimationLLMMeituan
0 likes · 22 min read
Meituan's Exploration and Practice in Advertising Algorithm: Information Flow Ad Estimation
DataFunSummit
DataFunSummit
Aug 15, 2024 · Artificial Intelligence

Building an LLM‑Driven Metric Platform for Data Democratization

This article explains how large language models (LLMs) can launch data democratization by constructing a metric platform that combines LLM agents, semantic layers, NL2SQL/NL2API pipelines, warehouse‑internal and external semantics, and showcases SwiftAgent/SwiftMetrics innovations, real‑world case studies, and future directions.

Big DataData DemocratizationLLM
0 likes · 13 min read
Building an LLM‑Driven Metric Platform for Data Democratization
21CTO
21CTO
Aug 11, 2024 · Artificial Intelligence

Demystifying LLMs: How Tokens, Training, and Transformers Power Generative AI

This article explains the fundamentals of large language models, covering tokenization, probability prediction, Markov chain basics, training data limitations, context windows, and the transition to neural network architectures like Transformers, while providing Python examples and insights into model scaling and the illusion of intelligence.

AILLMTransformer
0 likes · 18 min read
Demystifying LLMs: How Tokens, Training, and Transformers Power Generative AI
Architect
Architect
Aug 11, 2024 · Artificial Intelligence

Understanding Large Language Models: Tokens, Tokenization, and the Evolution from Markov Chains to Transformers

This article explains how generative AI models work by demystifying tokens, tokenization with tools like tiktoken, simple Markov‑chain training, the limitations of small context windows, and how modern LLMs use neural networks, transformers and attention mechanisms to predict the next token.

LLMMarkov chainTransformer
0 likes · 20 min read
Understanding Large Language Models: Tokens, Tokenization, and the Evolution from Markov Chains to Transformers
DataFunSummit
DataFunSummit
Aug 10, 2024 · Artificial Intelligence

Leveraging Large Language Models for Graph Recommendation System Optimization

This article reviews cutting‑edge research on integrating large language models with graph‑based recommendation systems, detailing four key strategies—LLM node embeddings, deep graph‑LLM fusion, model‑driven graph data training, and text‑modal enhancements—while analyzing representation learning, InfoNCE optimization, explainable recommendations, and extensive experimental validation.

ExplainabilityInfoNCELLM
0 likes · 18 min read
Leveraging Large Language Models for Graph Recommendation System Optimization
JavaEdge
JavaEdge
Aug 9, 2024 · Artificial Intelligence

Build a Graph‑Based LLM Agent with LangGraph: Step‑by‑Step Tutorial

This article introduces LangGraph, a Python library for creating stateful, multi‑agent LLM workflows, explains its loop, persistence, and human‑in‑the‑loop features, shows how to install it, and provides a complete code example that builds, runs, and reuses a searchable AI agent with thread‑level state saving.

AILLMLangChain
0 likes · 10 min read
Build a Graph‑Based LLM Agent with LangGraph: Step‑by‑Step Tutorial
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 9, 2024 · Artificial Intelligence

Testing 1M‑Token LLMs with a Novel Medal‑Insertion Benchmark

The article presents a practical method for evaluating 1‑million‑token LLMs by inserting structured medal data into a classic Chinese novel, provides a full Python script for the test, shares results on GLM‑4‑long, and discusses training techniques and open‑source resources for long‑context models.

AILLMPython
0 likes · 10 min read
Testing 1M‑Token LLMs with a Novel Medal‑Insertion Benchmark
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Aug 8, 2024 · Artificial Intelligence

MegaParse: A Precision Document Parser Built for LLMs

MegaParse is an open‑source document parser that transforms PDFs, Word, PPT, Excel and CSV files into LLM‑friendly formats, preserving full information, boosting processing efficiency, and enabling deeper semantic analysis, with quick‑start installation steps and a roadmap for future features.

AI toolsLLMOCR
0 likes · 4 min read
MegaParse: A Precision Document Parser Built for LLMs
DaTaobao Tech
DaTaobao Tech
Aug 7, 2024 · Artificial Intelligence

Overview of Large Model Development, AIGC Practices, and Prompt Engineering

The article surveys the rapid emergence of large AI models and AIGC, explains core concepts like AI, AGI, and LLMs, details prompt‑engineering techniques such as chain‑of‑thought, outlines a seven‑layer AIGC stack, discusses technical and ethical challenges, and highlights future multimodal and industry‑specific applications.

AIAIGCLLM
0 likes · 25 min read
Overview of Large Model Development, AIGC Practices, and Prompt Engineering
NewBeeNLP
NewBeeNLP
Aug 7, 2024 · Artificial Intelligence

Can Intuitive Fine‑Tuning Replace Expensive RLHF and DPO for LLM Alignment?

This article analyses the shortcomings of current large language model training methods such as SFT, RLHF and DPO, explains why they incur high data and compute costs, and introduces Intuitive Fine‑Tuning (IFT) with temporal residual connections as a cheaper yet effective alternative that better aligns training objectives with real generation tasks.

DPOIntuitive Fine-TuningLLM
0 likes · 15 min read
Can Intuitive Fine‑Tuning Replace Expensive RLHF and DPO for LLM Alignment?
Volcano Engine Developer Services
Volcano Engine Developer Services
Aug 6, 2024 · Artificial Intelligence

How an AI-Powered Bot Turns Excel Files into Interactive Reports

This article introduces an AI‑driven Smart Report Assistant Bot that automatically converts uploaded Excel files into recommended charts, allows users to customize reports, and details the underlying workflow—including Excel parsing, LLM‑generated SQL, dynamic table creation, chart rendering with ECharts, and image‑merging plugins.

AIBotECharts
0 likes · 8 min read
How an AI-Powered Bot Turns Excel Files into Interactive Reports
JD Tech Talk
JD Tech Talk
Aug 5, 2024 · Artificial Intelligence

An Introduction to STORM: An LLM‑Powered Knowledge Management System for Automated Research and Writing

This article introduces STORM, a Stanford‑developed large‑language‑model‑based knowledge‑management platform that automates topic research, outline generation, citation‑rich article writing, and iterative refinement through perspective‑guided questioning and simulated conversations, dramatically improving technical investigation efficiency.

AI toolsArticle GenerationLLM
0 likes · 7 min read
An Introduction to STORM: An LLM‑Powered Knowledge Management System for Automated Research and Writing
JD Cloud Developers
JD Cloud Developers
Aug 5, 2024 · Artificial Intelligence

How STORM Uses LLMs to Automate Technical Research and Writing

This article introduces STORM, a Stanford‑developed LLM‑based knowledge‑management system that automates topic research, outline creation, content generation with citations, and optimization through perspective‑guided questioning and simulated dialogue, dramatically improving technical investigation efficiency.

AI WritingLLMautomated research
0 likes · 5 min read
How STORM Uses LLMs to Automate Technical Research and Writing
Alibaba Cloud Native
Alibaba Cloud Native
Aug 2, 2024 · Cloud Native

How to Build an AI‑Native API Gateway with Higress: ChatGPT‑Next‑Web, RAG, Token Limits & More

This guide walks through creating a full‑featured AI‑native API gateway using Higress, covering architecture setup, AI agent integration, observability, content security, token rate limiting, caching, retrieval‑augmented generation, prompt templates, and intelligent request/response transformation with concrete configuration examples.

AIAPI GatewayLLM
0 likes · 11 min read
How to Build an AI‑Native API Gateway with Higress: ChatGPT‑Next‑Web, RAG, Token Limits & More
Open Source Tech Hub
Open Source Tech Hub
Jul 31, 2024 · Artificial Intelligence

Understanding LLMs, AI Agents, and Retrieval-Augmented Generation: Key Concepts and Challenges

This article explains the fundamentals of large language models, artificial general intelligence, AI-generated content, AI agents, retrieval‑augmented generation, knowledge bases, multimodal processing, fine‑tuning, alignment, tokens, vectors, and related tools, highlighting their capabilities, limitations, and practical considerations.

AI AgentArtificial IntelligenceLLM
0 likes · 14 min read
Understanding LLMs, AI Agents, and Retrieval-Augmented Generation: Key Concepts and Challenges
NewBeeNLP
NewBeeNLP
Jul 31, 2024 · Artificial Intelligence

How Continual Pre‑Training Boosts Llama‑3’s Chinese and Scientific Reasoning

This report presents a continual pre‑training approach that significantly enhances Llama‑3 (8B)’s Chinese language proficiency and scientific reasoning by using a carefully mixed corpus of existing and synthetic data, detailing the bilingual adaptation and synthetic‑enhancement stages, data‑mixing and curriculum strategies, and demonstrating strong results across multilingual and scientific benchmarks without sacrificing original capabilities.

LLMLlama-3benchmarking
0 likes · 9 min read
How Continual Pre‑Training Boosts Llama‑3’s Chinese and Scientific Reasoning
DataFunSummit
DataFunSummit
Jul 29, 2024 · Artificial Intelligence

Large Language Models for Recommendation Systems: Current Progress, Challenges, and Future Directions

This article reviews the state‑of‑the‑art applications of large language models in recommendation systems, summarizing background knowledge, recent advances such as LLM4Rec, various tuning strategies, agent‑based approaches, open research problems, and future directions for generative recommendation.

AILLMModel tuning
0 likes · 24 min read
Large Language Models for Recommendation Systems: Current Progress, Challenges, and Future Directions
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jul 25, 2024 · Artificial Intelligence

Designing Autonomous LLM Agents: Architecture, Memory, Planning, and Learning Strategies

This article surveys the design of autonomous large‑language‑model agents, detailing their modular architecture—including profiling, memory, planning, and execution—while also reviewing common profiling methods, memory structures, planning techniques, action strategies, and various learning approaches such as exemplar, human‑in‑the‑loop, and environment‑feedback training.

AIAutonomous AgentsLLM
0 likes · 36 min read
Designing Autonomous LLM Agents: Architecture, Memory, Planning, and Learning Strategies
phodal
phodal
Jul 24, 2024 · Artificial Intelligence

How to Build Trustworthy Coding Agents with Shire’s Custom RAG Workflow

This article explains how to use the Shire language to create reliable coding agents by defining custom RAG workflows, leveraging IDE APIs, code verification functions, and vector‑based search, with detailed examples, configuration snippets, and a roadmap for future enhancements.

AIIDELLM
0 likes · 10 min read
How to Build Trustworthy Coding Agents with Shire’s Custom RAG Workflow
Alibaba Cloud Native
Alibaba Cloud Native
Jul 24, 2024 · Cloud Native

How to Observe and Optimize LLM Applications with Alibaba Cloud ARMS

This article explains the challenges of deploying large language model (LLM) applications, outlines the need for end‑to‑end observability, and details Alibaba Cloud ARMS' LLM‑specific tracing, metrics, and Python agent solutions for monitoring, debugging, and performance optimization.

AILLMMetrics
0 likes · 20 min read
How to Observe and Optimize LLM Applications with Alibaba Cloud ARMS
DataFunSummit
DataFunSummit
Jul 24, 2024 · Artificial Intelligence

Overview of Large Language Model‑Based AI Agents: Architecture, Challenges, and Future Directions

This article reviews the emerging field of large language model‑based AI agents, outlining their overall architecture, key challenges such as role‑playing, memory, planning, and multi‑agent collaboration, and discusses future research directions and practical examples in user behavior simulation and software development.

AI AgentsLLMMemory Mechanisms
0 likes · 11 min read
Overview of Large Language Model‑Based AI Agents: Architecture, Challenges, and Future Directions
21CTO
21CTO
Jul 23, 2024 · Artificial Intelligence

How AI Coding Assistants Are Redefining Software Development

This article explores how large language model‑powered coding assistants boost developer productivity, shift the role of engineers toward higher‑level design and problem‑solving, and raise new responsibilities for code safety, performance, and ethical use in the evolving software development paradigm.

AI codingLLMdeveloper productivity
0 likes · 11 min read
How AI Coding Assistants Are Redefining Software Development
Model Perspective
Model Perspective
Jul 23, 2024 · Artificial Intelligence

Building Your Own AI Agent with LangChain: A Hands‑On Guide

This article walks through the author’s experience creating a custom AI agent using LangChain and OpenAI APIs, explains the concepts of AI agents and the ReAct reasoning framework, provides step‑by‑step code, discusses required libraries and APIs, and shares practical tips and challenges encountered.

AI AgentLLMLangChain
0 likes · 16 min read
Building Your Own AI Agent with LangChain: A Hands‑On Guide
DataFunSummit
DataFunSummit
Jul 22, 2024 · Artificial Intelligence

From BERT to LLM: Language Model Applications in 360 Advertising Recommendation

This talk explores how 360's advertising recommendation system leverages language models—from BERT to large‑scale LLMs—to improve user interest modeling, feature extraction, and conversion‑rate prediction, detailing practical challenges, engineering solutions, experimental results, and future research directions.

AdvertisingBERTLLM
0 likes · 18 min read
From BERT to LLM: Language Model Applications in 360 Advertising Recommendation
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.

LLMRetrieval Augmented GenerationTask-aware Decoding
0 likes · 18 min read
Task‑Aware Decoding (TaD): A Plug‑and‑Play Method to Mitigate Hallucinations in Large Language Models
DevOps
DevOps
Jul 21, 2024 · Artificial Intelligence

LLM Fundamentals, Applications, Prompt Engineering, RAG, and Agentic Workflows

This article provides a comprehensive overview of large language models (LLMs), covering their basic concepts, relationship with NLP, development history, parameter scaling, offline deployment, practical applications, prompt‑engineering frameworks, retrieval‑augmented generation, LangChain integration, agents, workflow orchestration, and future directions toward multimodal AI and AGI.

AI ApplicationsAgentArtificial Intelligence
0 likes · 36 min read
LLM Fundamentals, Applications, Prompt Engineering, RAG, and Agentic Workflows
21CTO
21CTO
Jul 21, 2024 · Artificial Intelligence

How JetBrains AI Boosts Code Completion and Refactoring in Rider

This article reviews JetBrains AI, an LLM‑powered assistant for JetBrains IDEs, exploring its code‑completion, code‑explanation, unit‑test generation, and refactoring capabilities through real C# examples and discussing its impact on developer workflows.

C++IDEJetBrains AI
0 likes · 8 min read
How JetBrains AI Boosts Code Completion and Refactoring in Rider
DataFunTalk
DataFunTalk
Jul 20, 2024 · Artificial Intelligence

Exploring and Applying Large Language Models in Recommendation Systems

The talk by Huawei Noah's Ark Lab researcher Wang Yichao presents a comprehensive exploration of large language models (LLMs) for recommendation systems, covering background challenges, the KAR and Uni-CTR projects, experimental results, and future directions for open‑world, generative recommendation pipelines.

KARLLMUni-CTR
0 likes · 13 min read
Exploring and Applying Large Language Models in Recommendation Systems
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 AgentsLLMMemory Management
0 likes · 9 min read
Beyond RAG: How Mem0 Gives Large Language Models Super Memory for Personalized AI Apps
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jul 18, 2024 · Artificial Intelligence

Why AI Agents Are the Next Frontier of Intelligent Systems

This article surveys the rapid rise of AI agents powered by large language models, explaining their core perception‑planning‑action loop, memory architectures, tool‑use mechanisms, self‑reflection techniques, and real‑world case studies while highlighting current challenges and future prospects for autonomous intelligent systems.

AI AgentAutonomous SystemsLLM
0 likes · 29 min read
Why AI Agents Are the Next Frontier of Intelligent Systems
Tencent Cloud Developer
Tencent Cloud Developer
Jul 18, 2024 · Artificial Intelligence

Exploring Large Language Models (LLM): Fundamentals, Applications, and Future Directions

Exploring Large Language Models, this article surveys their core concepts, evolution through Transformers, GPT and BERT, generation challenges, diverse applications such as QA, multimodal creation, summarization and retrieval‑augmented generation, prompt‑engineering frameworks and tools, LangChain‑based pipelines, AI‑driven agents, and future prospects toward domain‑specific use, multimodality, and AGI.

AIAgentLLM
0 likes · 35 min read
Exploring Large Language Models (LLM): Fundamentals, Applications, and Future Directions
DataFunSummit
DataFunSummit
Jul 17, 2024 · Artificial Intelligence

Overview of LLM‑Based Agents: Architecture, Key Challenges, and Future Directions

This article reviews the emerging field of large‑language‑model (LLM) based AI agents, outlining their overall architecture, core modules such as profiling, memory, planning and action, discussing current challenges, presenting concrete use‑cases, and highlighting promising research directions.

AI AgentLLMMemory Mechanism
0 likes · 11 min read
Overview of LLM‑Based Agents: Architecture, Key Challenges, and Future Directions
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 17, 2024 · Artificial Intelligence

How Alibaba Cloud Built Service‑Domain AI Agents: Design, Practice, and Results

This article explains how Alibaba Cloud designed and deployed large‑language‑model agents for its service domain, covering background, ideal LLM deployment, the shift from explanation to problem solving, the agent framework, practical implementation, automation trade‑offs, training, evaluation, and real‑world impact.

AI AgentAlibaba CloudLLM
0 likes · 20 min read
How Alibaba Cloud Built Service‑Domain AI Agents: Design, Practice, and Results
Java Tech Enthusiast
Java Tech Enthusiast
Jul 16, 2024 · Artificial Intelligence

LLMs Misjudge Simple Number Comparison: 9.11 vs 9.9

Recent tests reveal that popular large language models—including GPT‑4o, Gemini Advanced, and Claude 3.5—often claim 9.11 is larger than 9.9 because their tokenizers split the numbers, but rephrasing, zero‑shot chain‑of‑thought prompts, or treating the values as floating‑point numbers can correct the mistake, a pattern also seen variably in Chinese models.

AI evaluationLLMPrompt Engineering
0 likes · 7 min read
LLMs Misjudge Simple Number Comparison: 9.11 vs 9.9
JD Tech Talk
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
Alibaba Cloud Native
Alibaba Cloud Native
Jul 15, 2024 · Cloud Native

How AI-Driven Gateways Are Evolving to Meet LLM Demands

The article examines how AI-era large language model (LLM) applications impose new traffic, security, and scalability requirements on gateways, and explains how the Envoy‑based open‑source Higress gateway addresses these challenges with hot configuration updates, token‑based rate limiting, streaming support, and multi‑tenant capabilities.

AIInfraLLM
0 likes · 19 min read
How AI-Driven Gateways Are Evolving to Meet LLM Demands
Architect
Architect
Jul 13, 2024 · Artificial Intelligence

Practical Guide to Building LLM Products: Prompt Engineering, RAG, Evaluation, and Operations

This article provides a comprehensive, step‑by‑step guide for developing large‑language‑model (LLM) applications, covering prompt design techniques, n‑shot and chain‑of‑thought strategies, retrieval‑augmented generation, structured I/O, workflow optimization, evaluation pipelines, operational best practices, and team organization to create reliable, scalable AI products.

AI operationsEvaluationLLM
0 likes · 54 min read
Practical Guide to Building LLM Products: Prompt Engineering, RAG, Evaluation, and Operations
DataFunSummit
DataFunSummit
Jul 10, 2024 · Artificial Intelligence

Applying Large Language Models to Recommendation Systems at Ant Group

The article presents Ant Group's research on integrating large language models into recommendation pipelines, covering background challenges, knowledge extraction, teacher‑model distillation, efficient deployment, experimental results, and future directions to improve accuracy and reduce bias.

AILLMRecommendation Systems
0 likes · 13 min read
Applying Large Language Models to Recommendation Systems at Ant Group
JD Tech
JD Tech
Jul 10, 2024 · Artificial Intelligence

Implementing Retrieval‑Augmented Generation (RAG) with LangChain4j in Java

This article provides a step‑by‑step guide for Java engineers on building a Retrieval‑Augmented Generation (RAG) application using the LangChain4j framework, covering RAG fundamentals, environment setup, Maven integration, document loading, splitting, embedding with OpenAI, vector store management with Chroma, and prompt‑based LLM interaction.

JavaLLMRAG
0 likes · 35 min read
Implementing Retrieval‑Augmented Generation (RAG) with LangChain4j in Java
NewBeeNLP
NewBeeNLP
Jul 8, 2024 · Artificial Intelligence

How LLMs Transform Recommendation Systems: The LEARN Framework Explained

This article reviews the Kuaishou paper on adapting large language models for recommendation, detailing the LEARN framework's dual‑tower architecture, embedding generation, loss functions, and experimental results that address cold‑start and long‑tail challenges in modern recommender systems.

Cold StartInfoNCELLM
0 likes · 8 min read
How LLMs Transform Recommendation Systems: The LEARN Framework Explained
21CTO
21CTO
Jul 7, 2024 · Artificial Intelligence

How to Build a Secure Local LLM Chatbot with Ollama, Python, and ChromaDB

This tutorial walks you through creating a privacy‑preserving, locally hosted large language model chatbot using Ollama, Python 3, and ChromaDB, covering RAG fundamentals, GPU selection, environment setup, and full source code for a Flask‑based application.

ChromaDBLLMOllama
0 likes · 19 min read
How to Build a Secure Local LLM Chatbot with Ollama, Python, and ChromaDB
Architecture Development Notes
Architecture Development Notes
Jul 5, 2024 · Artificial Intelligence

Simplify Multi‑LLM Integration in Rust with the genai Library

genai is a Rust library that unifies the APIs of major large language models, offering a lightweight, native solution with simple chat‑focused examples, demonstrated through a dual‑model Rust program, and outlines future expansions such as additional model support, multimodal capabilities, and performance optimizations.

APIArtificial IntelligenceGenAI
0 likes · 6 min read
Simplify Multi‑LLM Integration in Rust with the genai Library
JD Tech
JD Tech
Jul 5, 2024 · Artificial Intelligence

Generative Recommendation Systems for JD Alliance Advertising: Architecture, Implementation, and Experimental Evaluation

This article surveys how large language models reshape recommendation systems, presents a generative RS framework tailored for JD Alliance advertising, details material representation, model input, training and inference pipelines, and reports extensive offline and online experiments demonstrating its effectiveness on sparse user data.

LLMLarge Language Modelse-commerce advertising
0 likes · 27 min read
Generative Recommendation Systems for JD Alliance Advertising: Architecture, Implementation, and Experimental Evaluation
Continuous Delivery 2.0
Continuous Delivery 2.0
Jul 3, 2024 · Artificial Intelligence

Applying Large Language Models to Software Engineering: Challenges, Cross‑File Editing Issues, Bug‑Fixing Evaluation, and SWE‑Bench Results

This article examines the practical challenges of using large language models in software development, including handling long contexts, cross‑file editing, bug‑fixing evaluation methods, and presents benchmark results from SWE‑Bench and its Lite subset to assess model capabilities.

Bug fixingCross-File EditingEvaluation
0 likes · 7 min read
Applying Large Language Models to Software Engineering: Challenges, Cross‑File Editing Issues, Bug‑Fixing Evaluation, and SWE‑Bench Results
DataFunTalk
DataFunTalk
Jul 2, 2024 · Artificial Intelligence

Application of Large Language Models in Recommendation Systems: Overview and Future Directions

This article provides a comprehensive overview of how large language models (LLMs) are applied in recommendation systems, covering two main paradigms—LLM+RS as a component and LLM as a standalone recommender—detailing their impact on pre‑training, fine‑tuning, prompting, and future research challenges.

Future DirectionsLLMPre‑training
0 likes · 6 min read
Application of Large Language Models in Recommendation Systems: Overview and Future Directions
AntTech
AntTech
Jul 2, 2024 · Artificial Intelligence

Design and Implementation of a Generalized Retrieval‑Augmented Generation (RAG) Framework with Graph RAG Support

This article surveys Retrieval‑Augmented Generation (RAG), analyzes the limitations of traditional vector‑based RAG, introduces Graph RAG that leverages knowledge graphs for more reliable context, proposes a universal RAG architecture compatible with vector, graph and full‑text indexes, and details its open‑source implementation, code components, testing, and future research directions.

AIEngineeringGraphRAGKnowledgeGraph
0 likes · 26 min read
Design and Implementation of a Generalized Retrieval‑Augmented Generation (RAG) Framework with Graph RAG Support
JD Retail Technology
JD Retail Technology
Jul 1, 2024 · Artificial Intelligence

Generative Recommendation Systems for JD Alliance Advertising: Design, Implementation, and Evaluation

This article surveys how large language models reshape recommendation systems, details a generative recommender framework for JD Alliance ads—including item representation, model input, training, and inference—presents extensive offline and online experiments, and discusses future optimization directions.

JD AllianceLLMgenerative recommendation
0 likes · 25 min read
Generative Recommendation Systems for JD Alliance Advertising: Design, Implementation, and Evaluation
Continuous Delivery 2.0
Continuous Delivery 2.0
Jul 1, 2024 · Artificial Intelligence

Language Is Primarily a Tool for Communication, Not Thought: Insights from a Recent Nature Study

A recent Nature paper by MIT and collaborators argues that language serves mainly for communication rather than thought, presenting neuroscientific evidence that reasoning can occur without linguistic processing and sparking debate about the implications for large language models and artificial intelligence.

AICognitionLLM
0 likes · 8 min read
Language Is Primarily a Tool for Communication, Not Thought: Insights from a Recent Nature Study
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 29, 2024 · Artificial Intelligence

AI in Software Engineering at Google: Progress and the Path Ahead

The article describes how Google has integrated AI, particularly large language models, into its internal software development tools to improve developer productivity, outlines the challenges faced, shares lessons learned, and outlines future directions for AI‑driven engineering assistance.

AIGoogleLLM
0 likes · 10 min read
AI in Software Engineering at Google: Progress and the Path Ahead
NewBeeNLP
NewBeeNLP
Jun 28, 2024 · Artificial Intelligence

Why Large Language Models Aren’t Magic: Understanding Compression and Prompt Engineering

This article demystifies large language models by comparing them to classic compression algorithms, explains how they compress massive data into compact parameters, explores their ability to learn abstract patterns, and provides practical insights into prompt engineering, sampling strategies, and multi‑step agent architectures for real‑world applications.

LLMagent architecturemodel compression
0 likes · 19 min read
Why Large Language Models Aren’t Magic: Understanding Compression and Prompt Engineering
Baobao Algorithm Notes
Baobao Algorithm Notes
Jun 27, 2024 · Artificial Intelligence

Engineering Data for R&D Large Language Models: From Pre‑training to Prompt Design

This article presents a comprehensive guide to data engineering for research‑focused large language models, covering domain‑adaptive pre‑training, supervised fine‑tuning, retrieval‑augmented generation, dataset construction, data cleaning pipelines, token‑izer adaptation, and prompt engineering best practices to boost model performance in specialized tasks.

Domain AdaptationFine‑TuningLLM
0 likes · 20 min read
Engineering Data for R&D Large Language Models: From Pre‑training to Prompt Design
Baidu Geek Talk
Baidu Geek Talk
Jun 26, 2024 · Artificial Intelligence

Build a Conversational 24‑Point Game with Baidu AppBuilder’s AI Agent

This guide walks through the complete workflow of creating an AI‑native 24‑point game using Baidu Cloud's AppBuilder, covering the three‑step methodology, Agent architecture, component design, custom workflow implementation, and practical tips for optimal model selection.

24-point gameAI native appAppBuilder
0 likes · 14 min read
Build a Conversational 24‑Point Game with Baidu AppBuilder’s AI Agent
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Jun 26, 2024 · Cloud Native

Securing LLM Calls with Alibaba Cloud ASM Service Mesh Using a Wasm Plugin

This article demonstrates how to protect large language model (LLM) requests in a cloud‑native environment by using Alibaba Cloud ASM service mesh and a custom Wasm plugin to dynamically inject API keys, enforce custom denial patterns, and optionally route requests through a private LLM for intelligent data‑leak detection.

KubernetesLLMService Mesh
0 likes · 13 min read
Securing LLM Calls with Alibaba Cloud ASM Service Mesh Using a Wasm Plugin
JavaEdge
JavaEdge
Jun 23, 2024 · Artificial Intelligence

Build a Cultural Name‑Generator with LangChain, Custom Prompts, and Output Parsers

This tutorial walks through installing LangChain, creating an LLM (via own GPU resources or third‑party APIs), designing parameterized prompt templates, implementing a custom output parser for structured results, and running a complete Python example that generates culturally specific names.

AILLMLangChain
0 likes · 7 min read
Build a Cultural Name‑Generator with LangChain, Custom Prompts, and Output Parsers
JavaEdge
JavaEdge
Jun 23, 2024 · Artificial Intelligence

What Is LangChain? Features, Pros, Cons, and Setup Guide

This article introduces LangChain, an open‑source framework for building LLM‑powered applications, outlines its key components such as prompts, chains, agents, and retrieval‑augmented generation, compares its advantages and drawbacks, and provides step‑by‑step instructions for setting up a Python development environment.

AILLMLangChain
0 likes · 7 min read
What Is LangChain? Features, Pros, Cons, and Setup Guide
DataFunTalk
DataFunTalk
Jun 21, 2024 · Artificial Intelligence

Fine‑tuning Large Language Models with Alibaba Cloud PAI: Practices, Techniques, and Deployment

This article introduces the Alibaba Cloud PAI platform for large language model (LLM) fine‑tuning, covering model‑training pipelines, performance‑cost trade‑offs, retrieval‑augmented generation, fine‑tuning methods such as full‑parameter, LoRA and QLoRA, model selection, data preparation, evaluation, and real‑world deployment examples.

AI platformLLMModel deployment
0 likes · 20 min read
Fine‑tuning Large Language Models with Alibaba Cloud PAI: Practices, Techniques, and Deployment
Data Thinking Notes
Data Thinking Notes
Jun 20, 2024 · Artificial Intelligence

Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA

This article explores how large language models can transform data workflows by using embeddings for semantic search, building private domain knowledge bases, generating SQL code from natural language with visualized results, and enhancing exploratory data analysis, outlining practical steps and benefits for enterprises.

EDALLMVector Database
0 likes · 7 min read
Leveraging LLMs for Data: Embedding Search, Knowledge Bases, Text2SQL, and EDA
NewBeeNLP
NewBeeNLP
Jun 20, 2024 · Artificial Intelligence

How LLMs Transform Recommendation Systems: Insights from Kuaishou’s LERAN Paper

This article analyzes Kuaishou’s May 2024 paper on LLM‑driven recommendation, detailing its dual‑tower architecture, contrastive learning of user and item embeddings, and a CVR‑auxiliary task that together improve cold‑start handling and boost both offline and online AUC metrics.

Cold StartIndustrial ApplicationItem Embedding
0 likes · 10 min read
How LLMs Transform Recommendation Systems: Insights from Kuaishou’s LERAN Paper
NewBeeNLP
NewBeeNLP
Jun 19, 2024 · Artificial Intelligence

Can Symbolic Chain‑of‑Thought Boost LLM Logical Reasoning?

The paper introduces SymbCoT, a Symbolic Chain‑of‑Thought framework that translates natural‑language problems into symbolic form, plans, solves, and verifies reasoning steps, achieving significantly higher logical reasoning performance than traditional CoT methods across multiple benchmark datasets.

ACL 2024Chain-of-ThoughtLLM
0 likes · 13 min read
Can Symbolic Chain‑of‑Thought Boost LLM Logical Reasoning?
dbaplus Community
dbaplus Community
Jun 18, 2024 · Artificial Intelligence

How to Effectively Evaluate RAG Systems: Metrics, Tools, and Best Practices

Evaluating Retrieval‑Augmented Generation (RAG) systems requires both component‑level and end‑to‑end metrics—such as context relevance, recall, answer relevance, and groundedness—and can be automated with tools like TruLens, RAGAS, LangSmith, and Langfuse, enabling systematic selection and optimization of LLM applications.

AI metricsLLMLangSmith
0 likes · 8 min read
How to Effectively Evaluate RAG Systems: Metrics, Tools, and Best Practices
JavaEdge
JavaEdge
Jun 17, 2024 · Artificial Intelligence

Build Simple LLM Agents with LangChain: A Hands‑On Tutorial

This guide explains what AI agents are, how they combine large language models with planning, memory, and tool use, and provides a step‑by‑step LangChain implementation—including environment setup, tool integration, and a runnable example that solves math and performs web searches.

LLMLangChainPython
0 likes · 6 min read
Build Simple LLM Agents with LangChain: A Hands‑On Tutorial
Bilibili Tech
Bilibili Tech
Jun 14, 2024 · Artificial Intelligence

Technical Report on the Index-1.9B Series: Model Variants, Pre‑training Optimizations, and Alignment Experiments

The report presents the open‑source Index‑1.9B family—base, pure, chat, and character variants—detailing benchmark results, pre‑training optimizations such as a normalized LM‑Head and deeper‑slim architectures, the importance of modest instruction data, alignment via SFT/DPO, role‑play enhancements with RAG, and acknowledges remaining safety and factual limitations.

AlignmentEvaluationInstruction Tuning
0 likes · 15 min read
Technical Report on the Index-1.9B Series: Model Variants, Pre‑training Optimizations, and Alignment Experiments
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 14, 2024 · Artificial Intelligence

AI Code Generation Tools: Benefits, Risks, and Top Choices

This article explains how AI-powered code generators create high‑quality code, outlines their capabilities such as language translation and documentation assistance, discusses safety and copyright concerns highlighted by research, and emphasizes that while popular, these tools should augment rather than replace developers.

AILLMcode generation
0 likes · 2 min read
AI Code Generation Tools: Benefits, Risks, and Top Choices
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jun 14, 2024 · Artificial Intelligence

How Alibaba Cloud OpenSearch Powers RAG: Insights from AICon 2024

In this talk, Alibaba Cloud's OpenSearch RAG team shares their year‑long journey of building retrieval‑augmented generation systems, covering data parsing, slicing, vectorization, hybrid retrieval, model fine‑tuning, performance optimizations, cost reduction, and future directions such as multimodal queries and agents.

AI SearchHybrid RetrievalLLM
0 likes · 25 min read
How Alibaba Cloud OpenSearch Powers RAG: Insights from AICon 2024
JD Cloud Developers
JD Cloud Developers
Jun 13, 2024 · Artificial Intelligence

How LLMs Are Redefining Recommender Systems for JD Union Ads

This article surveys the impact of large language models on recommendation systems, outlines generative recommender architectures, discusses challenges of JD Union advertising, presents a semantic‑ID based solution with training and inference details, and reports offline and online experimental results.

AICold StartLLM
0 likes · 22 min read
How LLMs Are Redefining Recommender Systems for JD Union Ads
JD Tech Talk
JD Tech Talk
Jun 13, 2024 · Artificial Intelligence

Generative Recommender Systems for JD Affiliate Advertising: Architecture, Methods, and Experimental Evaluation

This article surveys how large language models can reshape recommendation systems, describes the four-stage generative pipeline, details item representation techniques such as semantic IDs, presents a JD affiliate advertising use case with offline and online experiments, and outlines future optimization directions.

Cold StartLLMgenerative recommender
0 likes · 25 min read
Generative Recommender Systems for JD Affiliate Advertising: Architecture, Methods, and Experimental Evaluation
DataFunSummit
DataFunSummit
Jun 12, 2024 · Artificial Intelligence

Large Language Model (LLM) Powered Recommendation Systems: Overview, Techniques, Challenges, and Future Directions

This article reviews how large language models are transforming recommendation systems, covering their fundamentals, recent LLM‑enabled methods for representation, learning and generalization, challenges such as scalability, bias and privacy, and future research directions including personalized prompts and robust model integration.

LLMRecommendation Systemsmodel generalization
0 likes · 19 min read
Large Language Model (LLM) Powered Recommendation Systems: Overview, Techniques, Challenges, and Future Directions
DataFunSummit
DataFunSummit
Jun 10, 2024 · Artificial Intelligence

Xiaomi Agent Technology: Architecture, Prompt Management, and Evaluation

This article presents Xiaomi's work on LLM‑based Agent technology, covering its perception‑thinking‑action pipeline, technical framework, prompt management, executor and API platform, workflow, optimization strategies, evaluation metrics, and future directions for AI assistants.

AI assistantAgentEvaluation
0 likes · 17 min read
Xiaomi Agent Technology: Architecture, Prompt Management, and Evaluation
DaTaobao Tech
DaTaobao Tech
Jun 7, 2024 · Artificial Intelligence

Exploring AI Agent Integration in HandCat App: Architecture, Tool Management, and Implementation

The HandCat team designed an end‑to‑LLM pipeline that separates agent templates, tool protocols, and view layers, enabling LLM‑driven agents with memory, planning, and three tool types—general, selector, and interruptor—to safely manage sessions, handle errors, and balance granularity for performance within a commercial mobile app.

AI AgentAgent LabLLM
0 likes · 18 min read
Exploring AI Agent Integration in HandCat App: Architecture, Tool Management, and Implementation
JD Tech Talk
JD Tech Talk
Jun 7, 2024 · Artificial Intelligence

AI‑Powered JUnit Rule for Automatic Error Reporting to GPT

This tutorial shows Java engineers how to build a JUnit Rule that captures test failures, extracts the exception stack and source file, and automatically sends the information to OpenAI's GPT for analysis and code‑fix suggestions, complete with reusable data‑model classes and utility methods.

AIGPTJUnit
0 likes · 13 min read
AI‑Powered JUnit Rule for Automatic Error Reporting to GPT