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2671 articles · Page 26 of 27
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Feb 7, 2024 · Artificial Intelligence

Step-by-Step Guide to Building Multi‑Agent Applications with LangChain LangGraph in Google Colab

This tutorial walks through installing LangChain, LangGraph and related packages in Google Colab, configuring environment variables, defining search and Twitter‑writer tools, constructing a StateGraph workflow with supervisor logic, and executing a multi‑agent LLM pipeline using LangChain’s new multi‑agent capabilities.

AIGoogle ColabLLM
0 likes · 11 min read
Step-by-Step Guide to Building Multi‑Agent Applications with LangChain LangGraph in Google Colab
DataFunSummit
DataFunSummit
Feb 3, 2024 · Artificial Intelligence

Practical Application of Large Language Models in MaShang Consumer Finance: From Model Building to Deployment

This article details how MaShang Consumer Finance leverages large language models for sales, collection, and customer service, covering company background, AI research achievements, model training infrastructure, data‑quality and compliance challenges, prompt engineering, inference acceleration, evaluation methods, and lessons learned from real‑world deployment.

Data QualityLLMcompliance
0 likes · 21 min read
Practical Application of Large Language Models in MaShang Consumer Finance: From Model Building to Deployment
NewBeeNLP
NewBeeNLP
Feb 2, 2024 · Artificial Intelligence

ControlRec: Aligning LLMs with IDs to Boost Personalized Recommendations

ControlRec introduces heterogeneous feature matching and instruction contrastive learning to bridge the semantic gap between language models and discrete user/item IDs, enabling more effective personalized recommendation across multiple tasks such as rating prediction, sequential recommendation, and explanation generation.

ControlRecHeterogeneous Feature MatchingInstruction Contrast Learning
0 likes · 10 min read
ControlRec: Aligning LLMs with IDs to Boost Personalized Recommendations
Ctrip Technology
Ctrip Technology
Jan 26, 2024 · Artificial Intelligence

Implementing Plugin Functionality for a Large Language Model Chatbot Using Function Calling and Asynchronous Execution

This article explains how Ctrip's security R&D team built a web‑based LLM chatbot with version‑2.0 features such as plugin support, function calling, synchronous and asynchronous execution, WebSocket/Socket.IO communication, and provides full Python code examples for defining and invoking plugins.

AIBackendFunction Calling
0 likes · 15 min read
Implementing Plugin Functionality for a Large Language Model Chatbot Using Function Calling and Asynchronous Execution
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jan 22, 2024 · Artificial Intelligence

Prompt Engineering and CAMEL: Role‑Playing AI Agents for Automated Prompt Generation

This article explains how Prompt Engineering combined with the CAMEL framework enables role‑playing AI agents to automatically generate and manage prompts, illustrates the concept with a stock‑trading example, and provides Python code using LangChain to build a marketing‑automation agent for a small business.

AI agentsCAMELInception Prompting
0 likes · 11 min read
Prompt Engineering and CAMEL: Role‑Playing AI Agents for Automated Prompt Generation
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jan 21, 2024 · Artificial Intelligence

Understanding Pretraining and Fine‑Tuning of Large Language Models: Methods, Resources, and Practical Applications

This article explains the concepts of pretraining and fine‑tuning for large language models, compares full‑parameter, LoRA and QLoRA approaches, discusses resource consumption, introduces the ModelScope SWIFT framework with code examples, and shows how fine‑tuning can improve data‑visualisation tasks while reducing token usage.

LLMLoRAModelScope
0 likes · 24 min read
Understanding Pretraining and Fine‑Tuning of Large Language Models: Methods, Resources, and Practical Applications
Tencent Cloud Developer
Tencent Cloud Developer
Jan 16, 2024 · Frontend Development

Frontend Technology Review 2023 and Outlook 2024

The 2023 frontend review highlights TypeScript’s size and speed gains, ECMAScript 2023 features, evolving frameworks like React, Vue, Svelte, Angular and emerging Qwik, while Rust tooling, Bun, browser changes, AI‑driven low‑code, and WASM progress set the stage for 2024’s LLM‑powered, Rust‑centric, cross‑platform development.

BunD2CHarmonyOS
0 likes · 49 min read
Frontend Technology Review 2023 and Outlook 2024
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Jan 12, 2024 · Artificial Intelligence

Negative Sample Assisted Distillation for Large Language Models

The AAAI‑2024 paper introduces a Negative Sample Assisted Distillation framework—comprising Negative Assistance Training, Negative Calibration Enhancement, and Adaptive Self‑Consistency—that leverages both correct and incorrect reasoning examples to train a compact LLaMA‑7B student, achieving up to 75.75 % accuracy gains over fine‑tuning on MATH and improving out‑of‑domain benchmarks.

LLMModel Specializationchain-of-thought
0 likes · 13 min read
Negative Sample Assisted Distillation for Large Language Models
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 6, 2024 · Artificial Intelligence

How to Pick the Best Fine‑Tuning Data for LLMs with the Nuggets Method

This article explains the Nuggets approach for selecting a high‑quality subset of annotated instructions to fine‑tune large language models, describing its three inputs, the gold‑score computation based on perplexity improvement, empirical results on Alpaca, and practical considerations such as task‑set design.

LLMNuggetsdata selection
0 likes · 7 min read
How to Pick the Best Fine‑Tuning Data for LLMs with the Nuggets Method
DaTaobao Tech
DaTaobao Tech
Jan 5, 2024 · Mobile Development

Edge Deployment and Performance Optimization of Large Language Models with MNN

The upgraded mnn‑llm framework adds a unified llm‑export pipeline, cross‑platform inference with tokenizers and disk‑embedding, and ARM‑focused linear‑layer optimizations—including SIMD, hand‑written assembly and 4‑bit quantization—that dramatically speed up prefilling and achieve real‑time LLM conversation on mobile devices within a 2 GB memory budget, outperforming llama.cpp, fastllm and mlc‑llm.

ARM CPUEdge DeploymentLLM
0 likes · 17 min read
Edge Deployment and Performance Optimization of Large Language Models with MNN
DataFunSummit
DataFunSummit
Jan 4, 2024 · Big Data

YY Live Business Metric Governance Practice

This presentation details YY Live’s data product team’s end‑to‑end business metric governance practice, covering problem background, analysis, governance objectives, multi‑team collaboration, implementation steps, achieved efficiencies, and future directions leveraging large language models.

LLMSelf‑service analyticsbig data
0 likes · 16 min read
YY Live Business Metric Governance Practice
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 2, 2024 · Artificial Intelligence

Uncovering Mixtral‑8x7B: How MoE Experts Shape Performance and Training

This article analyses the Mixtral‑8x7B Mixture‑of‑Experts LLM, explains its gate‑driven 8‑expert architecture, presents a simplified PyTorch implementation, and reports a series of experiments that probe top‑2 gating during training, individual expert contributions, task‑specific pre‑training, the impact of expert count, and similarity with Mistral‑7B, ultimately offering hypotheses about its training pipeline.

LLMMixtralMixture of Experts
0 likes · 14 min read
Uncovering Mixtral‑8x7B: How MoE Experts Shape Performance and Training
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 29, 2023 · Artificial Intelligence

Overview of Major Benchmark Datasets for Evaluating Large Language Models

This article provides a comprehensive overview of major benchmark datasets—including CMMLU, MMLU, C‑Eval, GSM8K, Gaokao‑Bench, AGIEval, MATH, BBH, HumanEval, and MBPP—used to evaluate large language models' knowledge, reasoning, and coding abilities, and summarizes related leaderboards and evaluation tools.

Artificial IntelligenceLLMdataset
0 likes · 14 min read
Overview of Major Benchmark Datasets for Evaluating Large Language Models
Huolala Tech
Huolala Tech
Dec 28, 2023 · Artificial Intelligence

How Huolala Built a Low‑Code LLM Platform to Accelerate AI Agent Deployment

Huolala created a visual, drag‑and‑drop LLM application platform that streamlines AI integration, reduces development costs, and enables rapid deployment of agents across marketing, invitation, advertising, and modeling scenarios, boosting efficiency by over 98% while cutting integration time from hours to minutes.

AIAgentLLM
0 likes · 13 min read
How Huolala Built a Low‑Code LLM Platform to Accelerate AI Agent Deployment
DaTaobao Tech
DaTaobao Tech
Dec 27, 2023 · Artificial Intelligence

Deploying a Private LLM Knowledge Base on a MacBook

The guide walks through installing and quantizing the open‑source ChatGLM3‑6B model and the m3e‑base embedder on a MacBook, wrapping them with a FastAPI OpenAI‑compatible service, routing requests through a One‑API gateway, storing metadata in MongoDB and vectors in PostgreSQL pgvector, deploying FastGPT for RAG, ingesting data, and demonstrating 5‑7 second response times, while outlining future improvements.

ChatGLM3FastAPILLM
0 likes · 23 min read
Deploying a Private LLM Knowledge Base on a MacBook
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 27, 2023 · Artificial Intelligence

Comprehensive Overview of Large Language Models: Capabilities, Limitations, Deployment, and Future Trends

This article provides a detailed examination of large language models, covering their underlying technologies, capabilities and constraints, model families, training processes, cloud and edge deployment challenges, agent architectures, and emerging trends, offering practical insights for developers, product managers, and researchers.

AgentsArtificial IntelligenceLLM
0 likes · 43 min read
Comprehensive Overview of Large Language Models: Capabilities, Limitations, Deployment, and Future Trends
21CTO
21CTO
Dec 15, 2023 · Artificial Intelligence

Why 2024 Will Be the Year of AI Engineers and LLM‑Driven Apps

The article outlines five major AI engineering trends for 2024—including the rise of AI engineers, evolving LLM tech stacks, open‑source large models, vector databases, and AI agents—highlighting how these shifts will reshape application development and industry competition.

2024 trendsAI agentsAI engineering
0 likes · 9 min read
Why 2024 Will Be the Year of AI Engineers and LLM‑Driven Apps
DataFunSummit
DataFunSummit
Dec 15, 2023 · Artificial Intelligence

Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges

This article explores how large language models can be incorporated into recommender systems, discussing background challenges, specific integration points across the recommendation pipeline, practical implementation methods, experimental results, and future research directions, while highlighting industrial considerations and potential improvements.

Industrial ApplicationsLLMfeature engineering
0 likes · 20 min read
Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges
Data Thinking Notes
Data Thinking Notes
Dec 12, 2023 · Artificial Intelligence

Boosting Text‑to‑SQL Accuracy with Prompt Engineering and LLMs

This article examines the challenges of LLM‑based Text‑to‑SQL such as hallucinations, data‑security risks, and user input errors, and presents prompt‑engineering strategies, fine‑tuning comparisons, prompt types, code examples, and experimental results to improve reliability and cost‑effectiveness.

Artificial IntelligenceLLMLangChain
0 likes · 15 min read
Boosting Text‑to‑SQL Accuracy with Prompt Engineering and LLMs
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Dec 12, 2023 · Artificial Intelligence

How LangChain Powers AI Agents: Principles, Debugging, and Real‑World Optimizations

This article explains the concept of AI Agents in the large‑language‑model era, details LangChain's implementation mechanics, shares practical challenges and optimizations encountered by NetEase Cloud Music, and provides step‑by‑step code examples and performance insights for building robust AI Agents.

AI agentLLMLangChain
0 likes · 20 min read
How LangChain Powers AI Agents: Principles, Debugging, and Real‑World Optimizations
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 8, 2023 · Artificial Intelligence

Simplifying Transformer Blocks: Removing Residual Connections, LayerNorm, and Other Components without Losing Performance

A recent ETH Zurich paper shows that standard Transformer blocks can be drastically simplified by removing residual connections, LayerNorm, projection and value parameters, and even MLP sub‑block components, achieving up to 16% fewer parameters and comparable training speed and downstream performance on both GPT‑style decoders and BERT models.

AILLMSignal Propagation
0 likes · 11 min read
Simplifying Transformer Blocks: Removing Residual Connections, LayerNorm, and Other Components without Losing Performance
Sohu Tech Products
Sohu Tech Products
Dec 6, 2023 · Databases

GPTuner: LLM-Driven PostgreSQL Knob Tuning

GPTuner, an LLM‑driven system for PostgreSQL knob tuning developed by researchers at Sichuan University, demonstrates that knowledge processing, parameter selection, search‑range optimization, and a two‑stage Bayesian framework each significantly improve performance, while costing roughly 880 000 GPT‑4 tokens (≈ $30) with reusable knowledge.

GPTunerLLMPostgreSQL
0 likes · 9 min read
GPTuner: LLM-Driven PostgreSQL Knob Tuning
DataFunTalk
DataFunTalk
Dec 6, 2023 · Artificial Intelligence

Distributed Training Techniques and Quantitative Analysis for Large Language Models (GPT‑175B)

This article presents a comprehensive overview of state‑of‑the‑art distributed training methods for large language models, using GPT‑175B as a case study to analyze memory, communication, and compute overheads, and to recommend practical optimization strategies such as tensor, pipeline, and sequence parallelism, ZeRO‑1 optimizer, and selective activation checkpointing.

Distributed TrainingGPU memory optimizationLLM
0 likes · 22 min read
Distributed Training Techniques and Quantitative Analysis for Large Language Models (GPT‑175B)
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 6, 2023 · Artificial Intelligence

Multi-Agent Research Overview, Open-Source Implementations, and Design Considerations

This article reviews the background of multi‑agent systems, compares major open‑source frameworks such as AutoGen, MetaGPT, AgentVerse, and XAgent, discusses design principles, collaboration strategies, and offers conclusions on LLM‑driven versus SOP‑driven approaches for building multi‑agent applications.

AIAgent FrameworkAutoGen
0 likes · 15 min read
Multi-Agent Research Overview, Open-Source Implementations, and Design Considerations
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 5, 2023 · Artificial Intelligence

How to Efficiently Fine‑Tune Qwen LLMs on Alibaba Cloud PAI Lingjun

This guide walks you through setting up Alibaba Cloud PAI Lingjun resources, preparing Qwen‑7B/14B/72B models, preprocessing large‑scale WuDao data, configuring distributed training with Megatron‑LM, performing continued pre‑training and supervised fine‑tuning, and finally deploying the model as an online service via PAI‑EAS.

Alibaba CloudLLMMegatron-LM
0 likes · 27 min read
How to Efficiently Fine‑Tune Qwen LLMs on Alibaba Cloud PAI Lingjun
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Nov 30, 2023 · Artificial Intelligence

Mastering LLM Text Generation: Decoding Methods Explained

This review of the recent MindSpore NLP public class walks through the fundamentals of large language model text generation, detailing deterministic decoding such as greedy and beam search, stochastic sampling techniques like temperature, top‑k and top‑p, and advanced methods including constrained beam, contrastive, and assisted search, with illustrative examples.

Beam SearchGreedy SearchLLM
0 likes · 5 min read
Mastering LLM Text Generation: Decoding Methods Explained
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 29, 2023 · Artificial Intelligence

Building a Private LLM‑Powered Knowledge Base with LangChain and ChatGLM3

This article explains how to migrate personal notes into a private knowledge base by combining a large language model with an external vector store, detailing the concepts of tokenization, embedding, vector databases, and step‑by‑step deployment using LangChain‑Chatchat and the open‑source ChatGLM3 model.

ChatGLM3EmbeddingLLM
0 likes · 10 min read
Building a Private LLM‑Powered Knowledge Base with LangChain and ChatGLM3
DataFunSummit
DataFunSummit
Nov 20, 2023 · Artificial Intelligence

ModelScope Agents: Open‑Source LLM Agent Framework and Practical Guide

This article introduces ModelScope Agents, an open‑source LLM‑based agent framework that addresses limitations of GPT Store, outlines its features, provides installation and usage instructions, showcases a RPG game example, and invites the community to contribute to its roadmap.

AIAgent FrameworkLLM
0 likes · 7 min read
ModelScope Agents: Open‑Source LLM Agent Framework and Practical Guide
Baobao Algorithm Notes
Baobao Algorithm Notes
Nov 13, 2023 · Artificial Intelligence

Mastering LLM Fundamentals: Tokenizers, Layer Norm, and PEFT Explained

This article provides a comprehensive technical guide on large language model fundamentals, covering tokenizer construction methods such as BPE, WordPiece, and SentencePiece, detailed explanations of Layer Normalization variants, Deep Norm concepts with code, and an overview of parameter‑efficient fine‑tuning techniques like LoRA and PEFT.

Artificial IntelligenceLLMLayer Normalization
0 likes · 36 min read
Mastering LLM Fundamentals: Tokenizers, Layer Norm, and PEFT Explained
Data Thinking Notes
Data Thinking Notes
Nov 12, 2023 · Artificial Intelligence

Unlocking LLM Power: Semantic Search, Private Knowledge Bases, and Text‑to‑SQL for Data Teams

This article explores how large language models can boost data workflows by using embeddings for semantic retrieval, building domain‑specific knowledge bases for private Q&A, generating SQL code from natural language, and automating exploratory data analysis, offering practical steps and visual examples.

EmbeddingLLMexploratory data analysis
0 likes · 7 min read
Unlocking LLM Power: Semantic Search, Private Knowledge Bases, and Text‑to‑SQL for Data Teams
Baobao Algorithm Notes
Baobao Algorithm Notes
Nov 7, 2023 · Artificial Intelligence

A Complete Technical Guide to LLM Foundations, Advanced Topics, Fine‑Tuning, and LangChain Applications

This article provides an in‑depth technical overview of large language models (LLMs), covering core model families, architectural differences, emergent abilities, common challenges such as repetition and token limits, detailed fine‑tuning strategies including PEFT, practical guidance for training custom models, and a thorough introduction to the LangChain framework with code examples, core concepts, and troubleshooting tips for building LLM‑powered applications.

LLMLangChainVector Store
0 likes · 97 min read
A Complete Technical Guide to LLM Foundations, Advanced Topics, Fine‑Tuning, and LangChain Applications
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Nov 3, 2023 · Artificial Intelligence

Can LLMs Master Lifelong Learning? Exploring MoE and Continuous Adaptation

This article explains how large language models can achieve continual lifelong learning, outlines the key properties required, reviews mixture‑of‑experts (MoE) techniques—including sparse MoE, GShard, Switch Transformer, GLaM and PanGu‑Sigma—and discusses the remaining challenges such as model complexity, expert balancing and distributed communication overhead.

Artificial IntelligenceLLMLifelong Learning
0 likes · 9 min read
Can LLMs Master Lifelong Learning? Exploring MoE and Continuous Adaptation
DataFunSummit
DataFunSummit
Nov 1, 2023 · Artificial Intelligence

DataFunCon2023 Shenzhen: Program Overview and Session Highlights

DataFunCon2023 Shenzhen showcases a comprehensive program featuring expert talks on building Data+LLM applications, large-scale storage, cloud‑native architectures, metric systems, data governance, AB testing, and industry‑specific large language model use cases across finance, gaming, advertising, and more, providing valuable insights for practitioners and researchers alike.

AIGCArtificial IntelligenceLLM
0 likes · 50 min read
DataFunCon2023 Shenzhen: Program Overview and Session Highlights
Software Development Quality
Software Development Quality
Oct 27, 2023 · Artificial Intelligence

TestAgent: Open-Source 7B LLM for Multi-Language Test Generation

TestAgent introduces an open-source 7B large language model tailored for software testing, offering multi‑language test case generation, automatic assert completion, and a lightweight engineering framework with quick‑start scripts, performance benchmarks, and deployment options for various hardware accelerators.

AI modelLLMMulti-language Generation
0 likes · 10 min read
TestAgent: Open-Source 7B LLM for Multi-Language Test Generation
JD Retail Technology
JD Retail Technology
Oct 26, 2023 · Artificial Intelligence

Leveraging Large Language Models for Text-to-SQL: Prompt Design and End-to-End Pipeline

This article explains how large language models can be used to convert natural language queries into SQL statements, describes two main approaches—direct generation and fine‑tuned open‑source models—details prompt engineering techniques, and outlines an end‑to‑end pipeline that executes the generated SQL and summarizes results.

ChatGLMLLMSQL generation
0 likes · 7 min read
Leveraging Large Language Models for Text-to-SQL: Prompt Design and End-to-End Pipeline
phodal
phodal
Oct 19, 2023 · Operations

Can LLMs Revolutionize Code Review? Inside AutoDev’s AI‑Powered Approach

The article examines how rising code volume and AI‑generated snippets challenge traditional code review, proposes an LLM‑assisted workflow using AutoDev and DevOpsGenius, details prompt design, commit filtering, and implementation steps, and discusses the benefits and limitations for different team roles.

AI AutomationCode ReviewDevOps
0 likes · 9 min read
Can LLMs Revolutionize Code Review? Inside AutoDev’s AI‑Powered Approach
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
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 19, 2023 · Artificial Intelligence

Efficient LLM Deployment: Low‑Precision, Flash Attention, and Architecture Tricks

This article reviews the main memory and compute challenges of deploying large language models and presents practical solutions—including low‑precision arithmetic, flash attention, advanced positional embeddings, key‑value caching, and quantization techniques—backed by code examples and performance measurements on models such as OctoCoder.

Flash AttentionLLMTransformers
0 likes · 35 min read
Efficient LLM Deployment: Low‑Precision, Flash Attention, and Architecture Tricks
Architect
Architect
Oct 18, 2023 · Artificial Intelligence

Code Understanding: Techniques, Applications, and AI‑Driven Solutions

This article explores the fundamentals of code understanding, including static, dynamic, and non‑code analysis, presents a three‑layer architecture for scalable code comprehension, and demonstrates practical AI‑enhanced applications such as intelligent unit testing, dead‑code detection, and AI‑based static analysis within CI/CD pipelines.

AICI/CDLLM
0 likes · 16 min read
Code Understanding: Techniques, Applications, and AI‑Driven Solutions
AI Large Model Application Practice
AI Large Model Application Practice
Oct 18, 2023 · Artificial Intelligence

How to Extract and Embed Tables and Images from PDFs for Multimodal RAG

This article explains a practical approach to parsing PDFs containing text, tables, and images, using the open‑source Unstructured library and LlaVA model, then embedding each modality into a vector store with multi‑vector retrieval to enable accurate semantic search in private‑knowledge RAG pipelines, with optional LangChain integration.

LLMLangChainPDF processing
0 likes · 12 min read
How to Extract and Embed Tables and Images from PDFs for Multimodal RAG
Ximalaya Technology Team
Ximalaya Technology Team
Oct 18, 2023 · Artificial Intelligence

The Evolution of AI Agents: From Philosophy to Modern Implementations

Tracing AI agents from Aristotle’s and Zhuangzi’s philosophical notions through the coining of “agent” in computer science to today’s learning‑based systems powered by large language models, the article outlines key milestones, core components—LLM brain, memory, planning, tool use—and showcases applications such as AlphaGo, Siri, and autonomous platforms, while forecasting their expanding, industry‑wide ubiquity.

AI agentsArtificial IntelligenceAutonomous Systems
0 likes · 21 min read
The Evolution of AI Agents: From Philosophy to Modern Implementations
Model Perspective
Model Perspective
Oct 15, 2023 · Artificial Intelligence

How to Use Large Language Models Ethically in Math Modeling Contests

COMAP’s new policy outlines why and how teams in mathematical modeling competitions should responsibly employ large language models and generative AI, detailing guiding principles, risks, citation requirements, and ethical considerations to ensure fairness, transparency, and academic integrity.

AI PolicyGenerative AILLM
0 likes · 9 min read
How to Use Large Language Models Ethically in Math Modeling Contests
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.

EmbeddingLLMLangChain
0 likes · 19 min read
Demystifying Retrieval‑Augmented Generation: From Theory to Working Chatbot
21CTO
21CTO
Oct 12, 2023 · Frontend Development

How Vercel’s AI‑Powered v0 Tool Is Transforming Frontend Development

Vercel has launched v0, an AI‑driven tool that lets developers describe desired UI components in plain text and receive generated frontend code, streamlining creation, offering multiple design options, and shifting developer focus toward creativity and design.

AILLMVercel
0 likes · 4 min read
How Vercel’s AI‑Powered v0 Tool Is Transforming Frontend Development
Zhuanzhuan Tech
Zhuanzhuan Tech
Oct 11, 2023 · Artificial Intelligence

Building a ChatGPT‑Based Intelligent Customer Service System with BERT Classification and Knowledge Filtering

This article describes how to construct an intelligent customer‑service assistant using ChatGPT for natural‑language understanding, BERT for user‑question classification, and Sentence‑BERT for knowledge‑selection, detailing system architecture, prompt design, model training, performance results, and practical cost reductions.

BERTChatGPTKnowledge Filtering
0 likes · 16 min read
Building a ChatGPT‑Based Intelligent Customer Service System with BERT Classification and Knowledge Filtering
ByteFE
ByteFE
Oct 11, 2023 · Artificial Intelligence

CR Copilot: An Open‑Source LLM‑Based Code Review Assistant with Private Knowledge Base

This article describes the design and implementation of a code‑review assistant powered by open‑source large language models and a privately hosted knowledge base, covering background, pain points, system architecture, model selection, vector‑store integration, prompt engineering, diff parsing, and practical reflections.

AICode ReviewLLM
0 likes · 24 min read
CR Copilot: An Open‑Source LLM‑Based Code Review Assistant with Private Knowledge Base
DataFunTalk
DataFunTalk
Oct 10, 2023 · Artificial Intelligence

Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges

This article surveys how large language models can be incorporated into recommender systems, discussing their strengths and limitations, outlining where and how they can be applied across the recommendation pipeline, presenting recent research examples, and highlighting challenges and future directions for industrial deployment.

LLMfeature engineeringrecommender systems
0 likes · 20 min read
Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 10, 2023 · Artificial Intelligence

Create a Custom Enterprise Conversational Search with Alibaba Cloud OpenSearch Vector & LLM

This guide walks you through setting up Alibaba Cloud OpenSearch Vector Search and LLM Intelligent Q&A editions, covering environment preparation, instance creation, data source configuration, field and index setup, document ingestion, query processing, and a complete Java SDK demo for building a flexible enterprise conversational search system.

Alibaba CloudConversational AIJava SDK
0 likes · 20 min read
Create a Custom Enterprise Conversational Search with Alibaba Cloud OpenSearch Vector & LLM
Baidu Geek Talk
Baidu Geek Talk
Oct 9, 2023 · Artificial Intelligence

Code Understanding Technology: Building White-Box Software Knowledge Graph at Baidu

Baidu’s white‑box code understanding platform combines static, dynamic, non‑code and LLM‑based analyses in a three‑layer architecture that accelerates C/C++ processing ninefold, supports multiple languages, and powers applications such as intelligent unit testing, orphan‑function cleanup and AI‑driven risk detection, while future integration with models like GPT‑4 aims to enable multi‑turn code Q&A, automated refactoring and predictive testing.

ASTBaiduCI/CD
0 likes · 15 min read
Code Understanding Technology: Building White-Box Software Knowledge Graph at Baidu
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 8, 2023 · Interview Experience

Must‑Know Large‑Model Interview Questions for RLHF Candidates

The article shares a practitioner’s transition story from reinforcement‑learning‑focused game AI to large‑model work, outlines the challenges faced during job hunting at major Chinese tech firms, and provides a curated list of 23 technical interview questions covering PPO, RLHF, dataset evaluation, model fine‑tuning, and broader LLM concepts.

AI researchLLMRLHF
0 likes · 10 min read
Must‑Know Large‑Model Interview Questions for RLHF Candidates
21CTO
21CTO
Oct 4, 2023 · Artificial Intelligence

How LangStream Merges Data Streams with Generative AI for Real‑Time LLM Apps

LangStream, the new open‑source framework from DataStax, combines event‑driven data streaming with generative AI, offering seamless integration with vector databases like Astra DB, Milvus, and Pinecone, and providing a Kubernetes‑based runtime that enables real‑time LLM applications without extensive coding.

Data StreamingKubernetesLLM
0 likes · 7 min read
How LangStream Merges Data Streams with Generative AI for Real‑Time LLM Apps
AI Large Model Application Practice
AI Large Model Application Practice
Oct 4, 2023 · Artificial Intelligence

Controlling LLM‑Based AI Agents with the Open‑Source ‘Agents’ Framework

This article introduces the experimental open‑source project ‘Agents’, explains common challenges of LLM‑based AI agents, compares it with tools like AutoGPT, LangChain and MetaGPT, and demonstrates how its configuration‑driven SOP approach enables more controllable, multi‑agent interactions and easier deployment.

LLMSOPconfiguration
0 likes · 14 min read
Controlling LLM‑Based AI Agents with the Open‑Source ‘Agents’ Framework
HomeTech
HomeTech
Sep 26, 2023 · Artificial Intelligence

Integrating Large Language Models with Search for Automotive Knowledge Retrieval

This article explores how combining traditional keyword search with large language models (LLMs) enhances understanding of user intent, builds a robust automotive knowledge base, and delivers more accurate, context‑aware answers through a multi‑stage retrieval and generation pipeline.

AIAutomotiveLLM
0 likes · 17 min read
Integrating Large Language Models with Search for Automotive Knowledge Retrieval
AI Large Model Application Practice
AI Large Model Application Practice
Sep 25, 2023 · Artificial Intelligence

How LangSmith Turns LLM Debugging, Testing, and Production Monitoring into a Seamless Workflow

This article explores LangSmith, the experimental platform from the creators of LangChain, detailing how it tracks complex LLM reasoning, supports batch testing and evaluation of AI applications, and offers a community Hub for sharing prompts and chains, ultimately helping move LLM projects from prototype to production.

AI testingLLMLangSmith
0 likes · 10 min read
How LangSmith Turns LLM Debugging, Testing, and Production Monitoring into a Seamless Workflow
phodal
phodal
Sep 24, 2023 · Artificial Intelligence

Designing a JVM‑Based LLM Framework: Insights from Chocolate Factory

This article explores the design principles, architectural decisions, and practical code examples behind the Chocolate Factory framework, a JVM‑centric LLM development platform inspired by LangChain, LlamaIndex, Spring AI, and PromptFlow, highlighting SDK construction, RAG workflows, and prompt engineering challenges.

AI developmentJVMLLM
0 likes · 11 min read
Designing a JVM‑Based LLM Framework: Insights from Chocolate Factory
NetEase LeiHuo Testing Center
NetEase LeiHuo Testing Center
Sep 22, 2023 · Artificial Intelligence

Understanding Large Language Models and Prompt Engineering: A Practical Guide

This article provides an introductory overview of large language models (LLMs), compares popular models, explains their underlying principles, and offers practical guidance on prompt engineering, model evaluation, usage tips, and safety considerations, helping readers effectively select and apply LLMs in various scenarios.

AILLMLarge Language Models
0 likes · 44 min read
Understanding Large Language Models and Prompt Engineering: A Practical Guide
DataFunSummit
DataFunSummit
Sep 22, 2023 · Artificial Intelligence

Exploring Game AI Agents: Review, LLM‑Driven Exploration, and Future Directions

This article reviews the evolution of game AI agents, examines how large language models (LLMs) can drive new AI behaviors in games, and discusses practical case studies across genres such as Werewolf‑style, war‑SLG, and MOBA games, concluding with challenges and future research directions.

AI agentsLLMSelf-Play
0 likes · 31 min read
Exploring Game AI Agents: Review, LLM‑Driven Exploration, and Future Directions
21CTO
21CTO
Sep 21, 2023 · Artificial Intelligence

Falcon 180B vs Llama 2: Which Open‑Source LLM Leads the AI Race?

This article compares the open‑source large language models Falcon 180B and Meta’s Llama 2, detailing their parameter sizes, training data, licensing, variants, infrastructure, language support, and safety policies, while providing links to official resources and a side‑by‑side feature table.

AI comparisonFalcon 180BLLM
0 likes · 8 min read
Falcon 180B vs Llama 2: Which Open‑Source LLM Leads the AI Race?
Ximalaya Technology Team
Ximalaya Technology Team
Sep 18, 2023 · Artificial Intelligence

Understanding Autonomous and Autopilot AI Agents: Insights from Industry Experts

The article surveys the rise of LLM‑powered AI agents, defining them as LLM + memory + planning + tool use, contrasting fully autonomous agents with human‑guided autopilot/copilot variants, outlining their benefits, risks such as hallucinations and unsafe actions, and urging modular frameworks and oversight for reliable enterprise deployment.

AI agentsAgent FrameworkArtificial Intelligence
0 likes · 27 min read
Understanding Autonomous and Autopilot AI Agents: Insights from Industry Experts
Yunxuetang Frontend Team
Yunxuetang Frontend Team
Sep 15, 2023 · Frontend Development

Front-End Insights: Architecture, Code Review, Storage, and New Tools

This article explores front‑end architecture decisions between Vue and React, shares a comprehensive code‑review methodology from 13 years at Tencent, evaluates the most robust local storage solutions for large offline data, details two screen‑adaptation approaches for Vue 3/Vite, and introduces emerging technologies such as LangChain’s RAG and Agents and the high‑performance Bun 1.0 JavaScript runtime.

BunCode ReviewLLM
0 likes · 4 min read
Front-End Insights: Architecture, Code Review, Storage, and New Tools
DaTaobao Tech
DaTaobao Tech
Sep 13, 2023 · Artificial Intelligence

Integrating Large Language Models with Recommendation Systems: Paradigms, Methods, and Experiments

The article surveys how large language models can be integrated into recommendation systems, either as feature extractors or as end‑to‑end recommenders, showing that LLM‑derived semantics improve recall, ranking, diversity, and user experience, and outlining future multimodal, efficiency, and re‑ranking directions.

EmbeddingLLMMultimodal
0 likes · 19 min read
Integrating Large Language Models with Recommendation Systems: Paradigms, Methods, and Experiments
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Sep 13, 2023 · Artificial Intelligence

Pai‑Megatron‑Patch: Design Principles, Key Features, and End‑to‑End Usage for Large Language Model Training

This article introduces the open‑source Pai‑Megatron‑Patch tool from Alibaba Cloud, explains its non‑intrusive patch architecture, enumerates supported models and features such as weight conversion, Flash‑Attention 2.0, FP8 training with Transformer Engine, and provides detailed command‑line examples for model conversion, pre‑training, supervised fine‑tuning, inference, and RLHF reinforcement learning pipelines.

FP8LLMMegatron
0 likes · 19 min read
Pai‑Megatron‑Patch: Design Principles, Key Features, and End‑to‑End Usage for Large Language Model Training
Baobao Algorithm Notes
Baobao Algorithm Notes
Sep 12, 2023 · Artificial Intelligence

Why RTX 4090 Beats H100 for LLM Inference but Fails at Training

The article analyses the performance, memory, bandwidth and cost of NVIDIA H100, A100 and RTX 4090 GPUs, explains why the 4090 cannot handle large‑model training due to communication and memory limits, and shows how its high compute‑to‑price ratio makes it attractive for inference, backed by detailed parallelism calculations and cost‑per‑token estimates.

GPULLMParallelism
0 likes · 46 min read
Why RTX 4090 Beats H100 for LLM Inference but Fails at Training
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 12, 2023 · Artificial Intelligence

Compression as a Measure of Intelligence in Large Language Models

The article argues that a large language model's ability to compress data through next‑token prediction reflects its intelligence, reviews theoretical and empirical evidence linking compression efficiency to model scale, and proposes a circuit‑competition framework to explain emergent capabilities, in‑context learning, and fine‑tuning effects.

CompressionGPT-4LLM
0 likes · 58 min read
Compression as a Measure of Intelligence in Large Language Models
AI Large Model Application Practice
AI Large Model Application Practice
Sep 6, 2023 · Artificial Intelligence

Prompt Engineering vs Fine‑Tuning: How to Choose the Best Strategy for Reliable LLM Outputs

This article compares Prompt Engineering and Supervised Fine‑Tuning for large language models, explains their principles, showcases common prompt patterns such as Chain‑of‑Thought, ReAct and Self‑Ask, outlines fine‑tuning stages and trade‑offs, and provides practical guidance on selecting the most suitable approach for specific enterprise AI Agent scenarios.

AI agentLLMfine-tuning
0 likes · 17 min read
Prompt Engineering vs Fine‑Tuning: How to Choose the Best Strategy for Reliable LLM Outputs
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Sep 1, 2023 · Artificial Intelligence

Understanding Function Calling and ReAct for LLM Agents with LangChain

This article explains how large language models can act as agents by using OpenAI's Function Calling and the ReAct prompting paradigm, compares their trade‑offs, and demonstrates practical implementations with LangChain, including code examples for defining tools, invoking functions, and orchestrating multi‑step reasoning.

AIAgentFunction Calling
0 likes · 21 min read
Understanding Function Calling and ReAct for LLM Agents with LangChain
Architect
Architect
Aug 31, 2023 · Artificial Intelligence

Building a Custom LLM Chatbot with LangChain, ChromaDB, and LLaMA‑2

This tutorial explains how to leverage generative AI tools—including LLMs, embedding models, vector databases, and the LangChain framework—to create a custom chatbot that answers user queries using a knowledge base, with step‑by‑step code examples for Google Colab.

EmbeddingGenerative AILLM
0 likes · 15 min read
Building a Custom LLM Chatbot with LangChain, ChromaDB, and LLaMA‑2
DataFunSummit
DataFunSummit
Aug 30, 2023 · Databases

Milvus: An AI‑Native Vector Database for Large Language Model Applications

This article introduces Milvus, an open‑source, cloud‑native vector database designed for AI workloads, explains how it helps mitigate large‑model hallucinations, outlines its CVP architecture, showcases performance benchmarks, and explores diverse application scenarios and future directions for LLM‑vector database integration.

AILLMMilvus
0 likes · 13 min read
Milvus: An AI‑Native Vector Database for Large Language Model Applications
21CTO
21CTO
Aug 28, 2023 · Artificial Intelligence

What Is Code Llama? Meta’s Open-Source LLM for Generating Code

Code Llama, Meta’s specialized extension of Llama 2, is a large language model fine‑tuned on code data that can generate, complete, and debug software across multiple languages, supports up to 100 k tokens, and is freely available for research and commercial use.

Code LlamaLLMMeta-AI
0 likes · 5 min read
What Is Code Llama? Meta’s Open-Source LLM for Generating Code
21CTO
21CTO
Aug 26, 2023 · Artificial Intelligence

How MetaGPT Leverages SOP to Boost Multi‑Agent LLM Collaboration

MetaGPT is a meta‑programming framework that encodes standard operating procedures as prompts, enabling LLM‑driven multi‑agent systems to automatically generate software artifacts, coordinate roles, and build complex applications like a Blackjack CLI with near‑perfect task completion.

AI collaborationLLMMetaGPT
0 likes · 4 min read
How MetaGPT Leverages SOP to Boost Multi‑Agent LLM Collaboration
phodal
phodal
Aug 26, 2023 · Artificial Intelligence

How CoUnit Turns LLMs Into a Smart Team API for Faster Collaboration

CoUnit is an open‑source Rust‑based tool that uses local semantic search and LLMs to create a virtual team interface, enabling low‑cost, offline knowledge retrieval, API discovery, and cross‑team assistance for software development teams.

AI integrationLLMRust
0 likes · 7 min read
How CoUnit Turns LLMs Into a Smart Team API for Faster Collaboration
DataFunTalk
DataFunTalk
Aug 23, 2023 · Artificial Intelligence

Evaluating Large Language Model Item Encoders for Textual Collaborative Filtering in Recommendation Systems

This article investigates whether replacing traditional ID-based item encoders with massive LLMs such as GPT‑3 improves recommendation performance, by conducting extensive experiments on three real‑world datasets, analyzing performance limits, generality of item representations, and comparing against ID‑based and prompt‑based methods.

AIGPT-3LLM
0 likes · 15 min read
Evaluating Large Language Model Item Encoders for Textual Collaborative Filtering in Recommendation Systems
Ximalaya Technology Team
Ximalaya Technology Team
Aug 22, 2023 · Artificial Intelligence

Guidelines and Best Practices for Prompt Engineering with Large Language Models

The guide outlines prompt‑engineering best practices for large language models, distinguishing base and instruction‑tuned LLMs, emphasizing clear, structured, step‑by‑step prompts, handling hallucinations, iterating through idea‑code‑data cycles, applying techniques to summarization, reasoning and expansion, managing token costs, and providing concrete OpenAI API examples.

AIAPI usageLLM
0 likes · 14 min read
Guidelines and Best Practices for Prompt Engineering with Large Language Models
php Courses
php Courses
Aug 14, 2023 · Artificial Intelligence

Guide to the Five Most Powerful Large Language Models and How to Choose Them

This article explains the fundamentals of modern large language models, outlines the top five most powerful LLMs—including GPT‑4, Claude 2, Llama 2, Orca, and Cohere—and provides practical guidance on selecting and applying them across business and development use cases.

AI applicationsClaude 2GPT-4
0 likes · 9 min read
Guide to the Five Most Powerful Large Language Models and How to Choose Them
AI Large Model Application Practice
AI Large Model Application Practice
Aug 11, 2023 · Artificial Intelligence

How LLMs Are Revolutionizing Enterprise Apps: Scenarios, Architecture & Challenges

This article examines how large language models (LLMs) are reshaping enterprise applications by enabling natural‑language interfaces, automating workflows, and enhancing data analysis, while also detailing typical use cases, integration complexities, and practical solutions such as prompt engineering and vector‑database retrieval.

Artificial IntelligenceLLMenterprise applications
0 likes · 11 min read
How LLMs Are Revolutionizing Enterprise Apps: Scenarios, Architecture & Challenges
Architect's Guide
Architect's Guide
Aug 10, 2023 · Artificial Intelligence

Getting Started with LangChain: Building LLM Applications in Python

This tutorial introduces LangChain, an open‑source Python framework that provides unified model access, prompt management, memory, retrieval, and tool integration, enabling developers to quickly prototype AI‑driven applications using large language models and various external data sources.

AgentsLLMLangChain
0 likes · 13 min read
Getting Started with LangChain: Building LLM Applications in Python