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Prompt engineering

1342 articles · Page 12 of 14
Architect
Architect
Feb 12, 2025 · Artificial Intelligence

Master Prompt Engineering: A Universal Framework for LLMs

This article presents a comprehensive, step‑by‑step Prompt engineering framework—including role definition, problem description, goal setting, and requirement specification—augmented with techniques such as RAG, few‑shot examples, memory handling, and parameter tuning, enabling users to craft effective prompts for large language models across domains.

AI Prompt OptimizationFew-shotPrompt engineering
0 likes · 27 min read
Master Prompt Engineering: A Universal Framework for LLMs
Big Data Tech Team
Big Data Tech Team
Feb 9, 2025 · Artificial Intelligence

7 Proven Prompt Techniques to Unlock DeepSeek’s Full Potential

This guide presents seven practical prompt engineering tricks—ranging from precise requirement definition and contextual background provision to step‑by‑step decomposition, keyword tagging, iterative follow‑ups, tone/style adjustments, and model switching—that dramatically improve the relevance and quality of DeepSeek’s responses for work, learning, and creative tasks.

AI productivityDeepSeekLarge Language Model
0 likes · 6 min read
7 Proven Prompt Techniques to Unlock DeepSeek’s Full Potential
Infra Learning Club
Infra Learning Club
Feb 7, 2025 · Artificial Intelligence

Understanding LLM Agents: Architecture, Capabilities, and Key Challenges

This article explains what LLM agents are, their core components—brain, memory, planning, and tool use—illustrates how they handle complex queries through task decomposition, surveys notable frameworks, and discusses key challenges such as limited context, long‑term planning difficulties, output inconsistency, and prompt dependence.

AI ArchitectureLLM AgentsPlanning
0 likes · 15 min read
Understanding LLM Agents: Architecture, Capabilities, and Key Challenges
Code Mala Tang
Code Mala Tang
Jan 31, 2025 · Artificial Intelligence

Master DeepSeek: 7 Prompt Engineering Tricks to Boost AI Responses

This guide presents seven practical prompt‑engineering techniques—clear goals, structured queries, domain terminology, concrete examples, scoped questions, step‑by‑step breakdowns, and multi‑turn interactions—to help users get more accurate and useful answers from DeepSeek.

AI promptsDeepSeekLanguage Model
0 likes · 6 min read
Master DeepSeek: 7 Prompt Engineering Tricks to Boost AI Responses
DataFunSummit
DataFunSummit
Jan 31, 2025 · Artificial Intelligence

LLMOps: Building a Prompt‑Driven Engine for AI Operations

This article presents the concept of LLMOps—applying large language models to AIOps—by analyzing prompt challenges, introducing the LogPrompt engine for log analysis, describing a prompt‑learning data flywheel with CoachLM optimization, reporting experimental results, and outlining future multi‑modal directions.

AIOpsCoachLMData Flywheel
0 likes · 16 min read
LLMOps: Building a Prompt‑Driven Engine for AI Operations
Architect
Architect
Jan 27, 2025 · Artificial Intelligence

How to Build a Retrieval‑Augmented Generation QA Assistant for an Open Platform

This article details a step‑by‑step design of a RAG‑based intelligent Q&A assistant for the DeWu Open Platform, covering background, RAG fundamentals, system architecture, technology selection, prompt engineering with CO‑STAR, data preprocessing, vector store setup, LangChain.js implementation, similarity search, runnable chaining, debugging, and future prospects.

AILLMLangChain
0 likes · 28 min read
How to Build a Retrieval‑Augmented Generation QA Assistant for an Open Platform
DaTaobao Tech
DaTaobao Tech
Jan 24, 2025 · Artificial Intelligence

MktAI Assistant: AI‑Driven Marketing Data Query and Insight Platform

The MktAI Assistant combines LLM‑powered memory, skill planning, and tool‑calling with real‑time API data to replace slow, manual SQL dashboards, delivering sub‑minute, fresh, explainable marketing queries and attribution insights that boost decision speed, accuracy, and collaboration between data scientists and business users.

AI AgentChain-of-ThoughtFunction Calling
0 likes · 16 min read
MktAI Assistant: AI‑Driven Marketing Data Query and Insight Platform
21CTO
21CTO
Jan 22, 2025 · Artificial Intelligence

Understanding AI Agents: Core Components, Architecture, and Practical Implementation

This article consolidates Google's Kaggle whitepaper on AI Agents, explaining their definition, key characteristics, core components—model, tools, and orchestration layer—along with architectural diagrams, learning techniques, and practical deployment steps on Vertex AI, offering a comprehensive guide for building generative AI agents.

AI agentsModel-Tool-OrchestrationPrompt engineering
0 likes · 16 min read
Understanding AI Agents: Core Components, Architecture, and Practical Implementation
Architecture and Beyond
Architecture and Beyond
Jan 18, 2025 · Artificial Intelligence

Best Practices and Common Pitfalls When Using AI Programming Assistants

This article outlines practical guidelines for effectively using AI-powered coding assistants, emphasizing task decomposition, precise requirement definition, leveraging context memory, and addressing common challenges such as quota limits, context loss, code disruption, and handling complex problems to maximize development efficiency.

AI programmingContext ManagementPrompt engineering
0 likes · 15 min read
Best Practices and Common Pitfalls When Using AI Programming Assistants
Model Perspective
Model Perspective
Jan 14, 2025 · Artificial Intelligence

Quantifying AI Effectiveness: A Formulaic Model for Skills, Prompts, and Platforms

This article proposes a quantitative model that breaks AI usage effectiveness into three multiplicative factors—professional ability, prompt engineering skills, and AI platform capabilities—detailing each component, offering a prompt framework (BROKE), and providing tailored recommendations for beginners, competitors, and applied learners.

AI PlatformsAI effectivenessPrompt engineering
0 likes · 7 min read
Quantifying AI Effectiveness: A Formulaic Model for Skills, Prompts, and Platforms
Data Thinking Notes
Data Thinking Notes
Jan 7, 2025 · Databases

Unlocking LLM-Powered Text-to-SQL: From Basics to Cutting-Edge Techniques

This article provides a comprehensive overview of LLM-based Text-to-SQL technology, covering its background, evolution, challenges, various LLM-driven methods, benchmark datasets, evaluation metrics, and future research directions to guide researchers and practitioners in advancing natural language interfaces for databases.

EvaluationLLMPrompt engineering
0 likes · 18 min read
Unlocking LLM-Powered Text-to-SQL: From Basics to Cutting-Edge Techniques
Infra Learning Club
Infra Learning Club
Jan 7, 2025 · Artificial Intelligence

How GitHub Copilot Workspace Made Me Fear Unemployment

The author experiments with GitHub Copilot Workspace to automatically generate a WeChat mini‑program for family library management, documents the prompting process, code generation, bug fixes, UI tweaks, and reflects on the broader impact of AI‑driven development on programmers' future jobs.

AI code generationGitHub CopilotLLM
0 likes · 5 min read
How GitHub Copilot Workspace Made Me Fear Unemployment
DataFunTalk
DataFunTalk
Dec 14, 2024 · Artificial Intelligence

Advances and Practices of Large‑Model‑Powered Intelligent Development Tools

This article explores the evolution, enterprise adoption, and practical usage of large‑model‑driven intelligent development tools, covering code‑completion advancements, full‑repo indexing, CI/CD integration, prompt engineering, inline chat interactions, and best practices for developers to collaborate effectively with AI.

AIDeveloper ToolsPrompt engineering
0 likes · 32 min read
Advances and Practices of Large‑Model‑Powered Intelligent Development Tools
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Dec 13, 2024 · Artificial Intelligence

Optimizing Graph RAG: Boosting Global QA with Better Chunking, Prompts, and Entity Extraction

This article presents a comprehensive analysis of Graph RAG, detailing its implementation workflow, step‑by‑step execution guide, four targeted optimization strategies, and experimental validation that demonstrates significant improvements in global and local question answering for industry scenarios.

Graph RAGLLM OptimizationPrompt engineering
0 likes · 18 min read
Optimizing Graph RAG: Boosting Global QA with Better Chunking, Prompts, and Entity Extraction
CSS Magic
CSS Magic
Dec 4, 2024 · Frontend Development

Exploring AI-Powered Web Creation Platforms: A Hands‑On Look at Bolt and v0

This article demonstrates how browser‑based AI web‑creation platforms like Bolt and v0 can generate complete front‑end code from natural‑language prompts or design images, optimize prompts, preview results, and publish a site with a single click, while also showing how to download the code for further development.

AI web generationBoltCopyCoder
0 likes · 8 min read
Exploring AI-Powered Web Creation Platforms: A Hands‑On Look at Bolt and v0
Tencent Cloud Developer
Tencent Cloud Developer
Nov 27, 2024 · Artificial Intelligence

Tencent Cloud AI Code Assistant: Product Evolution, Architecture, and Technical Implementation

Tencent Cloud AI Code Assistant has evolved from token‑level IDE completions to LLM‑driven multi‑modal coding and chat features, employing a dual‑loop R&D system, Hunyuan‑based code models, and sophisticated trigger, prompt, stop, and display strategies to deliver context‑aware, secure, and efficient code generation within IDE and review environments.

AB testingAI code assistantAST analysis
0 likes · 15 min read
Tencent Cloud AI Code Assistant: Product Evolution, Architecture, and Technical Implementation
DaTaobao Tech
DaTaobao Tech
Nov 22, 2024 · Artificial Intelligence

AI Agents for Boosting Transaction System Efficiency

The article explains how AI agents, integrated into transaction systems, automate log analysis, generate test data via natural-language tools, and preserve expert knowledge, achieving at least a 50 % boost in issue-tracing efficiency, reducing debugging time, and empowering developers to focus on feature development and stability.

AI AgentPrompt engineeringdebugging
0 likes · 14 min read
AI Agents for Boosting Transaction System Efficiency
System Architect Go
System Architect Go
Nov 19, 2024 · Artificial Intelligence

Retrieval Augmented Generation (RAG) System Overview and Implementation with LangChain, Redis, and llama.cpp

This article explains the concept, architecture, and step‑by‑step implementation of Retrieval Augmented Generation (RAG), covering indexing, retrieval & generation processes, a practical LangChain‑Redis‑llama.cpp example on Kubernetes, code snippets, test results, challenges, and references.

AIEmbeddingLLM
0 likes · 6 min read
Retrieval Augmented Generation (RAG) System Overview and Implementation with LangChain, Redis, and llama.cpp
JD Tech
JD Tech
Nov 12, 2024 · Artificial Intelligence

Prompt Engineering: Concepts, Evolution, Techniques, and JD Logistics Application

This article explains what Prompt Engineering is, traces its development from early NLP commands to modern adaptive and multimodal prompting techniques, describes various prompting strategies such as Zero‑shot, Few‑shot, Chain‑of‑Thought, Auto‑CoT, and showcases a JD Logistics case study using these methods to classify product types with code examples.

AI Prompt DesignChain-of-ThoughtFew-shot
0 likes · 27 min read
Prompt Engineering: Concepts, Evolution, Techniques, and JD Logistics Application
JD Tech Talk
JD Tech Talk
Nov 11, 2024 · Artificial Intelligence

Prompt Engineering: Concepts, Evolution, Techniques, and a Logistics Application Case

This article explains what Prompt Engineering is, traces its development from early command‑based interactions to modern adaptive and multimodal prompting, details various prompting techniques such as zero‑shot, few‑shot, Chain‑of‑Thought, hallucination‑reduction methods, and demonstrates their practical use in a JD Logistics SKU piece‑type classification case with code examples.

AI promptingChain-of-ThoughtLLM applications
0 likes · 26 min read
Prompt Engineering: Concepts, Evolution, Techniques, and a Logistics Application Case
JD Cloud Developers
JD Cloud Developers
Nov 11, 2024 · Artificial Intelligence

Mastering Prompt Engineering: History, Techniques, and Real-World Applications

This article explains what Prompt Engineering is, traces its evolution from early NLP commands to modern adaptive and multimodal prompting, details core techniques such as Zero‑shot, Chain‑of‑Thought, Auto‑CoT, and reduction of hallucinations, and showcases a logistics case study using various prompting strategies.

AIChain-of-ThoughtLLM
0 likes · 26 min read
Mastering Prompt Engineering: History, Techniques, and Real-World Applications
NewBeeNLP
NewBeeNLP
Nov 7, 2024 · Artificial Intelligence

Tackling Large Model Hallucinations: Causes, Detection, and Mitigation Strategies

This article provides a comprehensive analysis of large language model hallucinations, detailing their definitions, classifications, root causes, detection techniques, and a wide range of mitigation approaches—including RAG pipelines, decoding strategies, and model‑enhancement methods—to improve reliability and safety in real‑world AI applications.

AI safetyHallucinationPrompt engineering
0 likes · 22 min read
Tackling Large Model Hallucinations: Causes, Detection, and Mitigation Strategies
DaTaobao Tech
DaTaobao Tech
Nov 1, 2024 · Artificial Intelligence

Multimodal Large Model for Voucher Verification: Prompt Engineering and Fine‑Tuning

By leveraging multimodal large models such as GPT‑4o and fine‑tuned Qwen‑VL, the study builds a prompt‑engineered and SFT‑enhanced voucher verification system that classifies product categories, detects diverse defects, and estimates problem counts, achieving up to 90 % accuracy and meeting real‑time business throughput requirements.

Multimodal AIPrompt engineeringe-commerce
0 likes · 10 min read
Multimodal Large Model for Voucher Verification: Prompt Engineering and Fine‑Tuning
Fighter's World
Fighter's World
Oct 26, 2024 · Artificial Intelligence

Key Considerations for Deploying Large Language Models in Cloud Services

The article reflects on Alibaba Cloud's large‑model deployments, outlines four service scenarios, examines three fundamental questions about foundation models, and offers a prioritized roadmap—including prompt engineering, RAG, and organizational changes—to effectively bring LLMs to production.

AI DeploymentAlibaba CloudCloud Services
0 likes · 8 min read
Key Considerations for Deploying Large Language Models in Cloud Services
58UXD
58UXD
Oct 22, 2024 · Artificial Intelligence

Boost Webtoon Production: How AI Powers Fast Comic Creation

This article explains how AI tools like GPT and Midjourney can streamline the entire webtoon creation process—from extracting core policy content to generating high‑quality comic panels—showing a complete workflow that reduces production time from weeks to days.

AIMidjourneyPrompt engineering
0 likes · 8 min read
Boost Webtoon Production: How AI Powers Fast Comic Creation
DataFunSummit
DataFunSummit
Oct 21, 2024 · Artificial Intelligence

Retrieval‑Augmented Generation (RAG) for Office Applications: Architecture, Challenges, and Practical Practices

This article introduces Retrieval‑Augmented Generation (RAG) as a solution to the hallucination, freshness, and data‑privacy issues of large language models, details its modular architecture, explains the layered system design and hybrid retrieval pipeline, and shares the practical challenges and engineering tricks encountered when deploying RAG in enterprise office scenarios.

AIHybrid RetrievalLarge Language Model
0 likes · 19 min read
Retrieval‑Augmented Generation (RAG) for Office Applications: Architecture, Challenges, and Practical Practices
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 21, 2024 · Artificial Intelligence

How to Build a Six Thinking Hats AI Agent: From Concept to Deployment

This article introduces the Six Thinking Hats framework, explains its benefits, describes AI agent concepts and platforms, and provides a step‑by‑step guide with prompt examples for creating a low‑cost, fully‑featured Six Thinking Hats assistant using generative AI tools.

AI AgentAgent platformsGenerative AI
0 likes · 13 min read
How to Build a Six Thinking Hats AI Agent: From Concept to Deployment
Tencent Advertising Technology
Tencent Advertising Technology
Oct 14, 2024 · Artificial Intelligence

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising

This paper presents the implementation and practice of generative retrieval based on Yuan large model in Tencent Advertising, addressing three key challenges: user intent capture, model alignment in advertising domain, and high-performance platform design under ROI constraints.

Generative RetrievalHigh-performance computingModel Optimization
0 likes · 17 min read
Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising
Architect
Architect
Oct 7, 2024 · Artificial Intelligence

Master Prompt Engineering: A Universal Framework for Building Effective LLM Prompts

This article presents a systematic, four‑part Prompt engineering framework—role definition, problem description, goal setting, and requirement specification—augmented with RAG, few‑shot examples, memory handling, and model‑parameter tuning, enabling developers to craft high‑quality prompts for large language models across diverse tasks.

Model ParametersPrompt engineeringRAG
0 likes · 28 min read
Master Prompt Engineering: A Universal Framework for Building Effective LLM Prompts
21CTO
21CTO
Sep 30, 2024 · Artificial Intelligence

How LLM‑Powered IDEs Can Cut Your Coding Time in Half

Using an LLM-powered IDE, the author built a full‑stack weekend project without writing a single line of code, discovering faster development cycles, new debugging habits, and the strengths and limits of AI assistants compared to traditional Google searches.

AI codingLLMPrompt engineering
0 likes · 10 min read
How LLM‑Powered IDEs Can Cut Your Coding Time in Half
Tencent Cloud Developer
Tencent Cloud Developer
Sep 27, 2024 · Artificial Intelligence

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

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

AI Prompt OptimizationPrompt engineeringRAG
0 likes · 26 min read
A Comprehensive Prompt Engineering Framework: Universal Templates, RAG, Few‑Shot, Memory, and Automated Optimization
Huolala Tech
Huolala Tech
Sep 26, 2024 · Artificial Intelligence

How LLM-Powered AI Assistants Transform Logistics Operations

This article details Huolala's exploration of large‑language‑model (LLM) based AI assistants across multiple business scenarios, describing their architecture, implementation challenges, prompt engineering techniques, and the progressive stages from professional assistants to multi‑agent systems that drive efficiency and innovation in logistics.

AI assistantLLMPrompt engineering
0 likes · 12 min read
How LLM-Powered AI Assistants Transform Logistics Operations
phodal
phodal
Sep 8, 2024 · Artificial Intelligence

Why Prompts Should Be Treated as Code: Engineering the Future of AI Agents

The article explores how prompts have evolved from simple text cues into executable, shareable agents, outlining engineering best practices, DSL‑plus‑runtime architecture, and the Shire Run platform that enables downloading, sharing, and future online execution of AI‑driven smart agents.

AI IDEExecutable PromptsPrompt engineering
0 likes · 9 min read
Why Prompts Should Be Treated as Code: Engineering the Future of AI Agents
iKang Technology Team
iKang Technology Team
Sep 5, 2024 · Artificial Intelligence

What Is LangChain? Overview, Core Advantages, Components, and Use Cases

LangChain is a modular framework that streamlines integration of large language models by providing unified model interfaces, prompt optimization, memory handling, indexing, chains, and agents, enabling developers to quickly build and deploy sophisticated NLP applications such as text generation, information extraction, and dynamic tool‑driven workflows across various industries.

AI FrameworkAgentsChains
0 likes · 6 min read
What Is LangChain? Overview, Core Advantages, Components, and Use Cases
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
Efficient Ops
Efficient Ops
Aug 28, 2024 · Artificial Intelligence

How Large Language Models Are Revolutionizing Banking Regulatory Interpretation

This article explores how AI-powered large language models enable Chinese commercial banks to automate, accurately match, and predict regulatory requirements, detailing new use‑cases, a prompt‑engineering framework, and the resulting efficiency and risk‑reduction benefits for the financial sector.

AIPrompt engineeringRegTech
0 likes · 7 min read
How Large Language Models Are Revolutionizing Banking Regulatory Interpretation
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 23, 2024 · Artificial Intelligence

Mastering Prompt Engineering: Advanced Techniques from Top AI Labs

This comprehensive guide examines cutting‑edge prompt‑engineering strategies—covering clear instruction design, role‑playing, separators, step‑by‑step workflows, external tools, systematic testing, and case studies from Anthropic, Google, and practical Img2Code applications—to help developers achieve more accurate and powerful interactions with large language models.

Prompt engineeringai-developmentbest practices
0 likes · 21 min read
Mastering Prompt Engineering: Advanced Techniques from Top AI Labs
DaTaobao Tech
DaTaobao Tech
Aug 19, 2024 · Frontend Development

Challenges and Solutions in AI-Powered Front-End Code Generation for B2C Platforms

The article details how Taobao’s AI team automated repetitive UI tasks for B2C front‑end development, achieving a 15 % efficiency gain across five projects, and outlines key challenges—prompt cost, low OCR accuracy, hallucinations, excess nodes, and customization variance—along with practical solutions such as a dedicated evaluation platform, OCR translation, model upgrades, prompt segmentation, output simplification, and a reusable component library.

AIPrompt engineeringUI automation
0 likes · 9 min read
Challenges and Solutions in AI-Powered Front-End Code Generation for B2C Platforms
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.

AILLMLarge Language Model
0 likes · 11 min read
Understanding Large Language Models: Training, Uses, and a Llama 3 Code Demo
Architect
Architect
Aug 2, 2024 · Artificial Intelligence

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

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

AI-nativeFunction CallingJava
0 likes · 40 min read
Building AI‑Native Applications with Spring AI: A Complete Tutorial
Tencent Cloud Developer
Tencent Cloud Developer
Jul 30, 2024 · Artificial Intelligence

A Systematic Guide to Prompt Engineering: From Zero to One

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

AI promptingChain-of-ThoughtLLM Optimization
0 likes · 51 min read
A Systematic Guide to Prompt Engineering: From Zero to One
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 ApplicationsAgentLLM
0 likes · 36 min read
LLM Fundamentals, Applications, Prompt Engineering, RAG, and Agentic Workflows
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
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 Cloud Developers
JD Cloud Developers
Jul 9, 2024 · Artificial Intelligence

How to Use Stable Diffusion for High‑Quality Promotional Images

Learn how to harness AI-powered Stable Diffusion models—via web UI, online platforms, or desktop apps—to create high‑quality promotional graphics, covering model types, samplers, seed settings, prompt crafting, weighting, and post‑processing techniques such as inpainting and upscaling.

AI image generationImage UpscalingPrompt engineering
0 likes · 11 min read
How to Use Stable Diffusion for High‑Quality Promotional Images
DataFunTalk
DataFunTalk
Jul 7, 2024 · Artificial Intelligence

Large Model Application Development: Architecture, Lifecycle, and Prompt Engineering

This article presents a comprehensive knowledge map for developing large‑model applications, covering a four‑layer technical architecture, the full development lifecycle, core elements such as prompt engineering and model fine‑tuning, evaluation methods, and practical case studies, offering guidance for both enterprises and startups.

AI application developmentEvaluationPrompt engineering
0 likes · 15 min read
Large Model Application Development: Architecture, Lifecycle, and Prompt Engineering
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
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
Architecture and Beyond
Architecture and Beyond
Jun 23, 2024 · Artificial Intelligence

AI Programming Paradigms Unveiled: Visual ComfyUI Workflows and LangChain LLM Apps

The article examines two emerging AI programming paradigms—visual, node‑based development with ComfyUI for image generation and modular LLM application construction with LangChain—detailing their architectures, key components, workflow examples, advantages, limitations, and practical guidance for leveraging these tools to boost development efficiency in the rapidly evolving AI landscape.

AIComfyUILLM applications
0 likes · 20 min read
AI Programming Paradigms Unveiled: Visual ComfyUI Workflows and LangChain LLM Apps
Code Mala Tang
Code Mala Tang
Jun 21, 2024 · Artificial Intelligence

How AI Turns UI Screenshots into Ready‑to‑Edit Front‑End Code

This article explains the Screenshot‑to‑Code project, detailing how AI‑driven image recognition converts UI screenshots into editable HTML, CSS, and JavaScript, describes the front‑end (React + Vite + Radix‑UI) and back‑end (Python + Poetry) architecture, showcases prompt engineering, and provides step‑by‑step setup instructions.

AI code generationPrompt engineeringPython Backend
0 likes · 14 min read
How AI Turns UI Screenshots into Ready‑to‑Edit Front‑End Code
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 20, 2024 · Artificial Intelligence

Build Your Own AI Image Editing Assistant with Alibaba Cloud PAI‑DSW

This guide walks you through using Alibaba Cloud's PAI‑DSW and the Free Prompt Editing algorithm to set up a personal AI‑generated content (AIGC) drawing assistant, covering environment setup, instance creation, WebUI parameter tuning, example edits, resource cleanup, and how to share your creations for rewards.

AIGCAlibaba CloudPAI-DSW
0 likes · 6 min read
Build Your Own AI Image Editing Assistant with Alibaba Cloud PAI‑DSW
Architecture & Thinking
Architecture & Thinking
Jun 19, 2024 · Artificial Intelligence

Build AI‑Native Apps Quickly with Spring AI: From Chat Models to RAG

This guide explains what an AI‑native application is, compares AI‑native and AI‑based approaches, and walks through Spring AI’s core features—including chat models, prompt templates, function calling, structured output, image generation, embedding, and vector stores—showing step‑by‑step code examples and how to assemble a complete AI‑native app with RAG support.

AI native applicationFunction CallingJava
0 likes · 43 min read
Build AI‑Native Apps Quickly with Spring AI: From Chat Models to RAG
JD Tech
JD Tech
Jun 19, 2024 · Artificial Intelligence

Advances in Large AI Models: Prompt Engineering, RAG, Agents, Fine‑Tuning, Vector Databases and Knowledge Graphs

This article surveys the rapid expansion of large AI models, covering prompt engineering, structured prompts, retrieval‑augmented generation, AI agents, fine‑tuning strategies, vector database technology, knowledge graphs, function calling, and their collective role in moving toward artificial general intelligence.

AIAgentFine‑tuning
0 likes · 23 min read
Advances in Large AI Models: Prompt Engineering, RAG, Agents, Fine‑Tuning, Vector Databases and Knowledge Graphs
DataFunTalk
DataFunTalk
Jun 17, 2024 · Artificial Intelligence

AI Agent Applications and Architecture in the 1688 E‑commerce Platform

This article summarizes the exploration of AI agents on the 1688 e‑commerce platform, covering the value of large language models, the agent solution architecture, deployment strategies, multi‑turn interaction design, AI‑driven innovation paradigms, and future planning discussed at DataFunCon 2024.

AI AgentMulti‑turn InteractionPrompt engineering
0 likes · 26 min read
AI Agent Applications and Architecture in the 1688 E‑commerce Platform
ShiZhen AI
ShiZhen AI
Jun 11, 2024 · Artificial Intelligence

Adding “Concise” to Prompts Cuts AI Costs by Over 20%

The article covers a global AI beauty contest, Microsoft’s security upgrades to its Recall device, step‑by‑step activation of ChatGPT’s background‑conversation mode, and a Johns Hopkins study showing that adding a “concise” instruction to prompts can slash AI response length by half and reduce API costs by more than 20% with little loss of accuracy.

AIAI-generated modelsChatGPT
0 likes · 4 min read
Adding “Concise” to Prompts Cuts AI Costs by Over 20%
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
Bilibili Tech
Bilibili Tech
Jun 7, 2024 · Artificial Intelligence

AI Development for Frontend Developers: From Basics to Agent Implementation

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

AgentFlowiseLangChain
0 likes · 13 min read
AI Development for Frontend Developers: From Basics to Agent Implementation
Aikesheng Open Source Community
Aikesheng Open Source Community
Jun 6, 2024 · Artificial Intelligence

Mastering ChatGPT Prompts and AI Assistant Techniques for Workplace Productivity

This article explores the rapid rise of ChatGPT, explains how to craft effective prompts using a role‑background‑task‑output formula, demonstrates its applications in writing, style transformation, and various professional scenarios, and introduces a new book and community giveaway related to AI assistants.

AI productivityAIGCChatGPT
0 likes · 10 min read
Mastering ChatGPT Prompts and AI Assistant Techniques for Workplace Productivity
Sohu Tech Products
Sohu Tech Products
Jun 5, 2024 · Artificial Intelligence

Retrieval Augmented Generation (RAG): Concepts, Workflow, and LangChain Implementation

The article outlines LLM issues such as hallucination, outdated knowledge, and data privacy, then explains Retrieval‑Augmented Generation—detailing its data‑preparation and query‑time retrieval workflow, demonstrates a full LangChain implementation, and contrasts RAG with fine‑tuning as complementary strategies for up‑to‑date, grounded responses.

LLMLangChainPrompt engineering
0 likes · 15 min read
Retrieval Augmented Generation (RAG): Concepts, Workflow, and LangChain Implementation
JavaEdge
JavaEdge
Jun 5, 2024 · Artificial Intelligence

Step‑by‑Step Guide to Building a Name‑Generator with LangChain and OpenAI

This tutorial walks through installing LangChain, creating an LLM with either self‑hosted or third‑party models, designing custom prompt templates, configuring output parsers for structured results, and running a complete Python example that generates culturally specific names using OpenAI's API.

LLMLangChainOpenAI
0 likes · 8 min read
Step‑by‑Step Guide to Building a Name‑Generator with LangChain and OpenAI
Zhixing ZXD Design Center
Zhixing ZXD Design Center
May 28, 2024 · Artificial Intelligence

How AIGC Is Revolutionizing Visual Design: Insights from the ZXD Team

This report examines the rapid rise of AI‑generated content (AIGC) in visual design since early 2023, detailing tool adoption, usage patterns across design roles, practical applications, case studies, knowledge accumulation, and the challenges and future outlook for integrating AI into design workflows.

AI toolsAIGCCase Studies
0 likes · 8 min read
How AIGC Is Revolutionizing Visual Design: Insights from the ZXD Team
CSS Magic
CSS Magic
May 16, 2024 · Artificial Intelligence

GPT-4o API Hands‑On Review: Blessing or Challenge for Developers?

The article evaluates GPT‑4o’s API by comparing its halved pricing, 50% higher token utilization, roughly double inference speed, and new prompt‑sensitivity quirks against GPT‑4‑Turbo and other models, then offers practical tips for integration and troubleshooting.

APIGPT-4oPrompt engineering
0 likes · 13 min read
GPT-4o API Hands‑On Review: Blessing or Challenge for Developers?
Baidu Geek Talk
Baidu Geek Talk
May 13, 2024 · Artificial Intelligence

Unveiling the Core Capabilities of Baidu Comate: An Intelligent Code Assistant

Baidu Comate, built on the Wenxin large model and embedded as an IDE plugin, delivers a 24/7 AI coding assistant that helps developers think, write, and review code through context‑aware generation, error fixing, test case creation, rapid sub‑600 ms responses, fill‑in‑the‑middle editing, prompt‑engineered personalization, AutoWork automation, and an open Comate+ platform for extensibility.

AI code assistantAI-driven developmentBaidu Comate
0 likes · 9 min read
Unveiling the Core Capabilities of Baidu Comate: An Intelligent Code Assistant
Baidu Tech Salon
Baidu Tech Salon
May 10, 2024 · Artificial Intelligence

Baidu Comate: Core Capabilities of Intelligent Code Assistant

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

AI codingBaidu ComateIDE plugin
0 likes · 7 min read
Baidu Comate: Core Capabilities of Intelligent Code Assistant
DataFunSummit
DataFunSummit
May 10, 2024 · Artificial Intelligence

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

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

AI OperationsLLMOpsModel Deployment
0 likes · 14 min read
LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions
Baidu App Technology
Baidu App Technology
May 8, 2024 · Artificial Intelligence

How AI Can Auto‑Generate Standardized Git Commit Messages

This article details the design, implementation, and evaluation of an AI‑powered tool that automatically creates compliant Git commit messages by leveraging large language models, custom plugins, and performance‑focused optimizations to improve developer productivity and commit quality.

AIGitLLM
0 likes · 16 min read
How AI Can Auto‑Generate Standardized Git Commit Messages
Baidu Geek Talk
Baidu Geek Talk
May 6, 2024 · Artificial Intelligence

How AI is Revolutionizing Mobile Test Case Creation with QAMate

The QAMate project demonstrates how generative AI can automatically generate, record, and maintain mobile UI, API, and requirement‑based test cases, dramatically reducing manual effort, improving stability, and creating a data‑driven feedback loop that continuously upgrades testing quality.

AI testingData FlywheelPrompt engineering
0 likes · 12 min read
How AI is Revolutionizing Mobile Test Case Creation with QAMate
DataFunSummit
DataFunSummit
May 6, 2024 · Artificial Intelligence

Advances, Model Types, and Open Challenges of AI‑Generated Content (AIGC) with XiaoBu’s Image Generation Progress

This article reviews the definition, key metrics, and major model families of AI‑generated content, details XiaoBu’s recent breakthroughs in image generation, and discusses open research problems such as evaluation gaps, transformer limitations, and the need for richer multimodal intelligence representations.

AIGCGaNPrompt engineering
0 likes · 14 min read
Advances, Model Types, and Open Challenges of AI‑Generated Content (AIGC) with XiaoBu’s Image Generation Progress
DataFunSummit
DataFunSummit
May 4, 2024 · Artificial Intelligence

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

This article provides a comprehensive overview of how large language models (LLMs) are integrated into recommendation systems, detailing two main paradigms—LLM as a component and LLM as a standalone system—while discussing their impact on retrieval, ranking, prompting, and outlining future research challenges such as multimodal recommendation, hallucination mitigation, bias reduction, and agent‑based approaches.

AIFuture DirectionsLLM
0 likes · 6 min read
Applications of Large Language Models in Recommendation Systems: Overview and Future Directions
IT Services Circle
IT Services Circle
May 1, 2024 · Artificial Intelligence

Summary of Andrew Ng’s AI Agent Talk: Models, Workflows, and Design Patterns

The article summarizes Andrew Ng’s presentation on AI agents, contrasting traditional single‑prompt large‑model usage with iterative agent‑based workflows, reporting experimental accuracy gains, and outlining four agent design patterns—reflection, tool use, planning, and multi‑agent collaboration—while discussing practical trade‑offs such as latency and token speed.

AI AgentLarge Language ModelPrompt engineering
0 likes · 7 min read
Summary of Andrew Ng’s AI Agent Talk: Models, Workflows, and Design Patterns
21CTO
21CTO
Apr 29, 2024 · Artificial Intelligence

Fine‑Tuning vs. Context Learning: Building Apps with the Emerging LLM Tech Stack

This article explores how developers can integrate large language models into applications by comparing fine‑tuning and context learning, detailing each method’s advantages and drawbacks, and presenting a four‑layer LLM tech stack—data, model, orchestration, and operations—with practical tooling examples.

AI StackContext LearningLLM
0 likes · 16 min read
Fine‑Tuning vs. Context Learning: Building Apps with the Emerging LLM Tech Stack
JavaEdge
JavaEdge
Apr 22, 2024 · Artificial Intelligence

Why Large Language Models Still Struggle and How to Fix Them

Large language models still suffer from limited memory, constrained context windows, outdated knowledge, inability to control external systems, and poor domain expertise, but the article outlines two main remedies—fine‑tuning (Model‑as‑a‑Service) and prompt‑engineering—detailing their mechanisms, suitable scenarios, and trade‑offs.

LLMModel as a ServicePrompt engineering
0 likes · 9 min read
Why Large Language Models Still Struggle and How to Fix Them
Baidu Geek Talk
Baidu Geek Talk
Apr 22, 2024 · Artificial Intelligence

Designing Effective Prompts for Large Language Models: Structure, Code Examples, and Regex Extraction

The article presents a systematic prompt template—comprising Instruction, Input Data, Context, and Output Indicator—demonstrates code examples for single‑ and multi‑task formatting, shows how clear markers enable regex extraction, and introduces Baidu’s PaddlePaddle Star River Community to simplify building reliable LLM‑driven applications.

AILarge Language ModelPrompt engineering
0 likes · 13 min read
Designing Effective Prompts for Large Language Models: Structure, Code Examples, and Regex Extraction
21CTO
21CTO
Apr 22, 2024 · Artificial Intelligence

Run Llama 3 Locally on PC/Mac: Ollama, LM Studio & GPT4All Guide

This guide walks you through three practical methods—using Ollama, LM Studio, and GPT4All—to install and run the open‑source Llama 3 model locally on Windows, macOS, or Ubuntu, including command‑line usage, Python integration, and prompt‑engineering techniques for formatted outputs.

GPT4AllLM StudioLlama3
0 likes · 5 min read
Run Llama 3 Locally on PC/Mac: Ollama, LM Studio & GPT4All Guide
New Oriental Technology
New Oriental Technology
Apr 19, 2024 · Artificial Intelligence

Effective Prompt Engineering for Large Language Models

This article explains how large language models work, why well‑crafted prompts are essential, and presents practical strategies—such as clarity, conciseness, focus, role‑setting, delimiters, few‑shot examples, and step‑by‑step instructions—to help users obtain accurate and relevant responses from AI systems.

AILLM strategiesLarge Language Model
0 likes · 12 min read
Effective Prompt Engineering for Large Language Models
DataFunTalk
DataFunTalk
Apr 13, 2024 · Artificial Intelligence

Integrating Generative AI with Business Intelligence: Design, Implementation, and Lessons from Baidu's ChatBI Platform

The article explores how generative AI transforms business intelligence by detailing BI's evolution, the ChatBI platform's architecture, NL2SQL challenges, performance and accuracy optimizations, and real‑world deployment outcomes that demonstrate reduced user barriers and enhanced analytical efficiency.

AIBusiness IntelligenceChatBI
0 likes · 13 min read
Integrating Generative AI with Business Intelligence: Design, Implementation, and Lessons from Baidu's ChatBI Platform
DataFunTalk
DataFunTalk
Apr 6, 2024 · Artificial Intelligence

Exploring Large Language Models for Recommendation Systems: Experiments and Insights

This article investigates how large language models can be applied to recommendation tasks, describing two usage strategies, various ranking approaches, experimental evaluations on multiple datasets, comparisons with traditional models, and analyses of prompt design, cost, and cold‑start capabilities.

LLMPrompt engineeringRanking
0 likes · 13 min read
Exploring Large Language Models for Recommendation Systems: Experiments and Insights
dbaplus Community
dbaplus Community
Apr 4, 2024 · Artificial Intelligence

10 Guiding Principles for Building LLM‑Powered Software Applications

This article outlines ten practical principles for designing applications with large language models, emphasizing a model‑first mindset, precision through interactive disambiguation, clear division of code and model responsibilities, data quality, handling uncertainty, and recognizing the limits of LLMs to build robust, maintainable software.

AI designData QualityLLM
0 likes · 13 min read
10 Guiding Principles for Building LLM‑Powered Software Applications
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Mar 30, 2024 · Artificial Intelligence

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

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

AI botCozeLLM
0 likes · 25 min read
Comprehensive Guide to Coze: AI Bot Development, Prompt Engineering, and Workflow Design
Sohu Tech Products
Sohu Tech Products
Mar 20, 2024 · Artificial Intelligence

Comparison of Base LLM and Instruction Tuned LLM

The diagram contrasts a Base LLM, which merely predicts the next word from training data and can continue stories or answer simple facts but may generate unsafe text, with an Instruction‑Tuned LLM that is fine‑tuned via RLHF to understand and follow commands, delivering more accurate, useful, and safe responses.

AIAI ApplicationsBASE model
0 likes · 7 min read
Comparison of Base LLM and Instruction Tuned LLM
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 17, 2024 · Artificial Intelligence

Why Role‑Playing LLMs Need More Than Assistant Fine‑Tuning

The article explains that current large language models lack true self‑awareness and act as assistants, so achieving convincing role‑playing behavior requires dedicated system prompts, specialized data, careful balance of continue pre‑training and general SFT, and evaluation methods to detect dissonance and preserve base capabilities.

AILLMPrompt engineering
0 likes · 19 min read
Why Role‑Playing LLMs Need More Than Assistant Fine‑Tuning
DataFunTalk
DataFunTalk
Mar 15, 2024 · Artificial Intelligence

Application of Agent Technology in Voice Assistant Scenarios

Senior algorithm engineer Qi Jianwei from Xiaomi presents a comprehensive overview of building a large‑model‑centric Agent framework for voice assistants, covering prompt design, information retrieval, RAG processes, and future optimization directions to enhance performance and stability.

AgentInformation RetrievalPrompt engineering
0 likes · 2 min read
Application of Agent Technology in Voice Assistant Scenarios
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Mar 10, 2024 · Artificial Intelligence

Building and Optimizing AI Bots with Coze: A Step‑by‑Step Low‑Code Guide

This article explains how to create, configure, and enhance AI bots on the Coze platform using a three‑step process, advanced workflow design, prompt engineering, and plugin integration, providing practical tips, code examples, and best‑practice recommendations for reliable content extraction and summarization.

AI botCozeKnowledge Base
0 likes · 18 min read
Building and Optimizing AI Bots with Coze: A Step‑by‑Step Low‑Code Guide
NewBeeNLP
NewBeeNLP
Mar 7, 2024 · Artificial Intelligence

How Sora is Redefining Large Vision Models: A Deep Dive into Technology, Limits, and Opportunities

This comprehensive review examines Sora, the first model capable of generating minute‑long, high‑quality videos from text, covering its historical background, core diffusion‑Transformer architecture, data preprocessing strategies, prompt engineering techniques, diverse applications, and the ethical and technical limitations that shape its future.

Multimodal AIPrompt engineeringSora
0 likes · 28 min read
How Sora is Redefining Large Vision Models: A Deep Dive into Technology, Limits, and Opportunities