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Prompt engineering

1342 articles · Page 11 of 14
Smart Era Software Development
Smart Era Software Development
Jun 12, 2025 · Artificial Intelligence

Anthropic’s Practical Guide to AI Agents: From Selection to Efficient Implementation

This article offers a detailed, Anthropic‑based guide on building effective AI agents and workflows, covering selection criteria, design patterns such as prompt chains, routing, parallelization, orchestrator‑worker and evaluation‑optimization, real‑world case studies, and concrete implementation recommendations that stress simplicity and composability.

AI agentsAnthropicLLM
0 likes · 26 min read
Anthropic’s Practical Guide to AI Agents: From Selection to Efficient Implementation
Nightwalker Tech
Nightwalker Tech
Jun 11, 2025 · Artificial Intelligence

Turn Your AI Coding Assistant into a Critical Mentor, Not Just a Tool

This guide explains how to shift AI coding tools like Cursor, Windsurf, and RooCode from simple code generators into proactive mentors that critique, suggest improvements, and adopt multiple specialized modes, while also covering prompt design, multi‑round dialogue, and practical code examples.

AILarge Language ModelPrompt engineering
0 likes · 15 min read
Turn Your AI Coding Assistant into a Critical Mentor, Not Just a Tool
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 11, 2025 · Artificial Intelligence

From Chat to Autonomous Agents: Architecture, ReAct, Prompt Engineering

This article chronicles the evolution from simple chat interactions to sophisticated autonomous agents, detailing stages of LLM development, ReAct reasoning, memory management, tool integration, and practical implementation using the browser-use project, while offering prompt design insights and future directions for AI agents.

AI AgentLLMMCP
0 likes · 30 min read
From Chat to Autonomous Agents: Architecture, ReAct, Prompt Engineering
Architecture & Thinking
Architecture & Thinking
Jun 11, 2025 · Artificial Intelligence

Accelerate LLM App Development with Eino: A Go Framework Walkthrough

Eino is an open‑source Golang framework for building large‑model applications, offering reusable components, robust orchestration, clean APIs, best‑practice templates, and full‑cycle DevOps tools, with code examples for both Ollama and OpenAI modes, plus streaming and normal output options.

GoLLMOpenAI
0 likes · 10 min read
Accelerate LLM App Development with Eino: A Go Framework Walkthrough
Data Thinking Notes
Data Thinking Notes
Jun 10, 2025 · Artificial Intelligence

Unlocking AI Agents: Architecture, Tools, and Real‑World Applications

This article provides a comprehensive overview of generative AI agents, detailing their core components—model, tools, and orchestration layer—explaining cognitive architectures, tool types, learning strategies, and practical development with LangChain and Vertex AI, while highlighting future prospects and challenges.

AI AgentLangChainPrompt engineering
0 likes · 24 min read
Unlocking AI Agents: Architecture, Tools, and Real‑World Applications
Su San Talks Tech
Su San Talks Tech
Jun 10, 2025 · Artificial Intelligence

Unlock AI-Powered Diagramming: 5 Proven Methods to Automate Your Charts

This guide shows programmers how to harness AI—especially Claude 4 via Cursor—to instantly generate professional diagrams such as flowcharts, UML, SVG, Canvas dashboards, and mind maps, offering step‑by‑step prompts, code examples, tool comparisons, and advanced tips for rapid, high‑quality visual documentation.

AI diagrammingMermaidPlantUML
0 likes · 19 min read
Unlock AI-Powered Diagramming: 5 Proven Methods to Automate Your Charts
Smart Era Software Development
Smart Era Software Development
Jun 7, 2025 · Artificial Intelligence

Why Traditional Programmers Are Becoming Obsolete: Insights from Anthropic CPO Mike Krieger

In a candid podcast, Anthropic CPO Mike Krieger reveals that up to 95% of Claude Code’s output is AI‑generated, discusses how engineering and product roles are being redefined, examines the challenges of merge queues and product strategy, and shares lessons from the rise and shutdown of the Artifact news app.

AI programmingAnthropicClaude
0 likes · 37 min read
Why Traditional Programmers Are Becoming Obsolete: Insights from Anthropic CPO Mike Krieger
Architecture and Beyond
Architecture and Beyond
Jun 7, 2025 · Artificial Intelligence

Does AI Really Simplify Software Development? Uncovering Hidden Complexities

The article examines how AI can speed up code generation yet fails to reduce the fundamental complexities of software development, shifting challenges to new areas such as prompt engineering, consistency, changeability, and invisibility, and argues that future developers must master AI to manage, not replace, complexity.

AI programmingPrompt engineeringcode generation
0 likes · 9 min read
Does AI Really Simplify Software Development? Uncovering Hidden Complexities
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Jun 6, 2025 · Artificial Intelligence

Tackling the Top Challenges of Retrieval‑Augmented Generation (RAG)

The article enumerates common pitfalls of Retrieval‑Augmented Generation—such as missing content, low‑rank document misses, context limits, format errors, incomplete answers, scalability bottlenecks, complex PDF extraction, data‑quality issues, domain adaptation gaps, hallucinations, and feedback‑loop deficiencies—and offers concrete mitigation strategies ranging from data cleaning and prompt design to hybrid search, hierarchical retrieval, document compression, and automated evaluation.

Data QualityHybrid SearchLLM
0 likes · 9 min read
Tackling the Top Challenges of Retrieval‑Augmented Generation (RAG)
Code Mala Tang
Code Mala Tang
Jun 5, 2025 · Artificial Intelligence

Mastering LLM Prompts: Proven Techniques to Get Precise Answers

By rethinking how we interact with large language models—using role‑play, task decomposition, chain‑of‑thought, ReAct, and other advanced prompting strategies—readers can transform generic ChatGPT answers into precise, context‑aware responses, leveraging pattern recognition and context windows for superior AI assistance.

AI reasoningChain-of-ThoughtLLM techniques
0 likes · 21 min read
Mastering LLM Prompts: Proven Techniques to Get Precise Answers
DaTaobao Tech
DaTaobao Tech
Jun 4, 2025 · Artificial Intelligence

Understanding Large Language Model Architecture, Parameters, Memory, Storage, and Fine‑Tuning Techniques

This article provides a comprehensive overview of large language models (LLMs), covering their transformer architecture, parameter counts, GPU memory and storage requirements, and detailed fine‑tuning methods such as prompt engineering, data construction, LoRA, PEFT, RLHF, and DPO, along with practical deployment and inference acceleration strategies.

DPOLLMLoRA
0 likes · 17 min read
Understanding Large Language Model Architecture, Parameters, Memory, Storage, and Fine‑Tuning Techniques
Smart Era Software Development
Smart Era Software Development
Jun 1, 2025 · Artificial Intelligence

Harrison Chase’s Key Insights on the Future of AI Agents

In his Interrupt 2025 keynote, LangChain founder Harrison Chase outlines the four core skills required of modern “Agent Engineers,” explains why multi‑model architectures, prompt‑driven context, and cross‑functional teamwork are essential, and reveals how LangGraph, LangSmith and the Open Agent Platform aim to solve current deployment and observability challenges for production‑grade AI agents.

AI agentsAI observabilityAgent deployment
0 likes · 19 min read
Harrison Chase’s Key Insights on the Future of AI Agents
Model Perspective
Model Perspective
May 30, 2025 · Artificial Intelligence

Why Large Language Models Are Just Mathematical Functions: A Rational Perspective

The article argues that large language models are fundamentally mathematical functions that model human language, emphasizing their role as simplified representations, explaining their structural nature, sources of errors, the importance of prompts as boundary conditions, and the need for clear usage assumptions to avoid anthropomorphic misconceptions.

AI FundamentalsPrompt engineeringlarge language models
0 likes · 11 min read
Why Large Language Models Are Just Mathematical Functions: A Rational Perspective
Instant Consumer Technology Team
Instant Consumer Technology Team
May 29, 2025 · Artificial Intelligence

API vs GUI Agents: How to Choose the Right LLM Automation Approach

This article examines the evolution of large language model agents, contrasting API‑based agents that use predefined function calls with GUI‑based agents that interact with visual interfaces, and explores hybrid strategies, orchestration tools, RAG techniques, and practical guidelines for selecting the optimal paradigm.

API vs GUIHybrid automationLLM Agents
0 likes · 34 min read
API vs GUI Agents: How to Choose the Right LLM Automation Approach
Alibaba Cloud Developer
Alibaba Cloud Developer
May 28, 2025 · Artificial Intelligence

Unlocking LLM Fine‑Tuning: From Architecture to LoRA, DPO and Deployment

This article provides a comprehensive guide to large language model fine‑tuning, covering model architecture, parameter and memory calculations, prompt engineering, data construction, LoRA and PEFT techniques, reinforcement learning methods such as DPO, and practical deployment workflows on internal platforms.

Fine‑TuningLLMLoRA
0 likes · 21 min read
Unlocking LLM Fine‑Tuning: From Architecture to LoRA, DPO and Deployment
Coder Circle
Coder Circle
May 28, 2025 · Artificial Intelligence

Core AI Concepts Every Spring AI Developer Should Know

This article explains fundamental AI concepts—including models, prompts, prompt templates, embeddings, tokens, structured output, data integration, RAG, and tool calling—and shows how Spring AI simplifies their use for Java developers building intelligent applications.

AI modelsPrompt engineeringRAG
0 likes · 13 min read
Core AI Concepts Every Spring AI Developer Should Know
phodal
phodal
May 27, 2025 · Industry Insights

Surviving the AI Code Dump: 7 Practical Strategies from AutoDev Workbench

This article shares the seven practical practices discovered while building AutoDev Workbench, detailing how AI‑assisted demand analysis, rapid UI prototyping, adaptive front‑end generation, focused refactoring, precise context feeding, automated testing, and lint‑type safeguards can turn chaotic AI‑generated code into a scalable, maintainable development workflow.

AI programmingCI/CDPrompt engineering
0 likes · 14 min read
Surviving the AI Code Dump: 7 Practical Strategies from AutoDev Workbench
Frontend AI Walk
Frontend AI Walk
May 27, 2025 · Artificial Intelligence

Vibe Coding in the AI Era: Opportunities and Challenges

The article examines Vibe Coding, an AI‑driven programming approach that lets developers generate software from natural‑language prompts, outlining its efficiency gains, lower entry barriers, cross‑domain collaboration benefits, as well as code‑quality, debugging, over‑reliance risks, and practical guidelines for responsible use.

AI-assisted programmingPrompt engineeringVibe Coding
0 likes · 15 min read
Vibe Coding in the AI Era: Opportunities and Challenges
Tencent Technical Engineering
Tencent Technical Engineering
May 23, 2025 · Artificial Intelligence

The Evolution, Challenges, and Future Directions of AI Agents

An in‑depth overview traces the development of AI agents from early LLM milestones to modern “class‑Agent” models, examines core components such as memory, tool use, planning and reflection, analyzes current limitations, and outlines emerging solutions like workflows, multi‑agent systems, and model‑as‑product paradigms.

AI AgentAgentic workflowPrompt engineering
0 likes · 40 min read
The Evolution, Challenges, and Future Directions of AI Agents
DaTaobao Tech
DaTaobao Tech
May 21, 2025 · Artificial Intelligence

Mastering CursorRules: Fine‑Tune Your AI Coding Assistant for Smarter, Consistent Code

This guide explains how to use CursorRules to precisely control the behavior of the Cursor AI programming assistant, covering the rule file structure, global versus project‑specific configurations, rule types, practical examples, best‑practice tips, integration with external documentation, and community resources for continuous improvement.

AI programmingCursorRulesPrompt engineering
0 likes · 19 min read
Mastering CursorRules: Fine‑Tune Your AI Coding Assistant for Smarter, Consistent Code
Continuous Delivery 2.0
Continuous Delivery 2.0
May 19, 2025 · Artificial Intelligence

12 Proven Tips to Supercharge Your AI Code Editor Cursor

Discover twelve practical techniques—from setting clear project rules and crafting precise prompts to modular development, test‑driven generation, context management, and model selection—that help developers maximize productivity and code quality when working with AI‑powered editors like Cursor, Windsurf, or CodeBuddy.

AICursorPrompt engineering
0 likes · 15 min read
12 Proven Tips to Supercharge Your AI Code Editor Cursor
AIWalker
AIWalker
May 18, 2025 · Artificial Intelligence

YOLOE: Open‑Source Real‑Time Anything Detector Beats YOLO‑World v2

YOLOE unifies object detection and segmentation in a single efficient model that supports text, visual, and prompt‑free inference, introduces RepRTA, SAVPE, and LRPC strategies, and achieves higher AP with up to three‑fold lower training cost and 1.4× faster inference on GPUs and mobile devices, as demonstrated by extensive LVIS and COCO experiments.

Prompt engineeringReal-timeYOLOE
0 likes · 29 min read
YOLOE: Open‑Source Real‑Time Anything Detector Beats YOLO‑World v2
Youzan Coder
Youzan Coder
May 16, 2025 · Artificial Intelligence

Intelligent Address Recognition: AI‑Assisted Hybrid Solution and Prompt Engineering

This article describes how a hybrid architecture that combines third‑party address‑recognition APIs with large‑language‑model (LLM) processing, along with carefully engineered prompts and a TSV output format, dramatically improves address parsing accuracy and latency in a retail checkout scenario.

AIHybrid ArchitectureLLM
0 likes · 12 min read
Intelligent Address Recognition: AI‑Assisted Hybrid Solution and Prompt Engineering
Eric Tech Circle
Eric Tech Circle
May 15, 2025 · Frontend Development

Generate Complete Multi‑File UI Prototypes with One Prompt Using Claude 3.7 and Cursor

The author shares a hands‑on experience with Anthropic's Claude 3.7 and the Cursor AI editor, identifies key pain points of fragmented code generation, and presents a redesigned prompt that produces all HTML prototype files in a single request, complete with a reusable template, usage scenarios, and visual results.

Claude 3.7Cursor AIPrompt engineering
0 likes · 7 min read
Generate Complete Multi‑File UI Prototypes with One Prompt Using Claude 3.7 and Cursor
Architect
Architect
May 14, 2025 · Artificial Intelligence

How Qwen3 Controls Hybrid Reasoning with the enable_thinking Parameter

This article explains how Qwen3 implements hybrid (fast/slow) reasoning by using the enable_thinking flag in the tokenizer's apply_chat_template method, detailing the underlying Jinja2 chat template, example prompts, the effect of toggling the flag, and design considerations for future autonomous thinking control.

AI modelChatMLHybrid Reasoning
0 likes · 13 min read
How Qwen3 Controls Hybrid Reasoning with the enable_thinking Parameter
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
May 14, 2025 · Artificial Intelligence

How AI Powers an Intelligent SQL Assistant for Query Optimization

This article details the design and implementation of an AI‑driven Intelligent SQL Assistant that automates query parsing, index recommendation, execution‑plan visualization, and supports SQL generation, diagnosis, and explanation across multiple dialects, while outlining its layered architecture, core modules, code examples, and future enhancements.

AIPrompt engineeringSQL
0 likes · 14 min read
How AI Powers an Intelligent SQL Assistant for Query Optimization
Alimama Tech
Alimama Tech
May 12, 2025 · Artificial Intelligence

Universal Recommendation Model (URM): A General Large‑Model Recall System for Advertising

The article presents the Universal Recommendation Model (URM), a large‑language‑model‑based recall framework that integrates world knowledge and e‑commerce expertise through knowledge injection and prompt‑driven alignment, achieving significant offline recall gains and a 3.1% increase in ad consumption while meeting high‑QPS, low‑latency production constraints.

AdvertisingLarge Language ModelMultimodal
0 likes · 17 min read
Universal Recommendation Model (URM): A General Large‑Model Recall System for Advertising
G7 EasyFlow Tech Circle
G7 EasyFlow Tech Circle
May 9, 2025 · Artificial Intelligence

How LLMs + Python Are Redefining Data Analysis: A Practical Guide

This article explains how large language models combined with Python's data‑science ecosystem can automate metadata extraction, data cleaning, and analysis tasks—illustrated with a step‑by‑step Titanic passenger dataset case study, complete prompts, code snippets, and best‑practice recommendations.

LLMPandasPrompt engineering
0 likes · 18 min read
How LLMs + Python Are Redefining Data Analysis: A Practical Guide
Youzan Coder
Youzan Coder
May 8, 2025 · Artificial Intelligence

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

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

AI assistantAily platformKnowledge Base
0 likes · 24 min read
Building and Optimizing a Store Smart Assistant with Aily: Architecture, Workflow, and Practical Lessons
Frontend AI Walk
Frontend AI Walk
May 7, 2025 · Artificial Intelligence

How Cursor AI Coding Tool Transforms Development Workflow

The article introduces Cursor, an AI‑powered coding assistant, outlines its supported large models, demonstrates practical front‑end use cases such as automatic layout creation, button logic, screenshot‑to‑code generation, error fixing and code cleanup, and reflects on prompt engineering and tool selection.

AI coding assistantCursorPrompt engineering
0 likes · 6 min read
How Cursor AI Coding Tool Transforms Development Workflow
Alibaba Cloud Developer
Alibaba Cloud Developer
May 7, 2025 · Artificial Intelligence

What Is an AI Agent? Understanding the Shift from Chatbots to Intelligent Automation

This article explores the concept of AI agents, contrasting them with traditional software and chatbots, outlines their core components, workflow, and the technological and market forces driving their evolution, and provides practical guidance for improving agent performance and choosing between workflow and LLM approaches.

AI AgentLLMPrompt engineering
0 likes · 24 min read
What Is an AI Agent? Understanding the Shift from Chatbots to Intelligent Automation
Eric Tech Circle
Eric Tech Circle
May 6, 2025 · Artificial Intelligence

How to Deploy Qwen3-30B-A3B Locally and Unlock Its Full AI Potential

This article walks through the complete process of installing the Qwen3-30B-A3B large language model on a personal computer using LM Studio, evaluates its reasoning, creative, multilingual, and coding abilities with detailed prompts, and shares practical tips for optimizing local deployment and prompt design.

AI evaluationLM StudioPrompt engineering
0 likes · 12 min read
How to Deploy Qwen3-30B-A3B Locally and Unlock Its Full AI Potential
Architecture and Beyond
Architecture and Beyond
Apr 26, 2025 · Artificial Intelligence

Four Essential Mindset Shifts for AI‑First Software Development

The article outlines four critical mindset transformations—adopting an AI‑first workflow, embracing commander‑level strategic thinking, continuously learning from AI, and building a composite human‑AI collaboration framework—to help developers stay competitive and extract maximum value from emerging AI programming tools.

AIPrompt engineeringmindset shift
0 likes · 24 min read
Four Essential Mindset Shifts for AI‑First Software Development
Tencent Technical Engineering
Tencent Technical Engineering
Apr 25, 2025 · Artificial Intelligence

Practical Guide to Building Effective AI Agents and Workflows

Fred’s practical guide expands Anthropic’s “Build effective agents” by offering a technical selection framework, clear definitions of agents versus workflows, a suite of reusable design patterns such as prompt‑chain routing and orchestrator‑worker loops, real‑world case studies, and concrete implementation tips that emphasize simplicity, transparency, and effective tool‑prompt engineering.

AI agentsAgent DesignLLM workflows
0 likes · 25 min read
Practical Guide to Building Effective AI Agents and Workflows
phodal
phodal
Apr 25, 2025 · Artificial Intelligence

How AutoDev Turns Prompts into Custom Local AI Coding Agents

This article analyzes the limitations of current AI coding assistants like Copilot and introduces AutoDev's local agent system, which lets developers define, compose, and extend AI agents through declarative prompts and configuration, enabling private, context‑aware, multi‑step coding workflows.

AI agentsAutoDevPrompt engineering
0 likes · 6 min read
How AutoDev Turns Prompts into Custom Local AI Coding Agents
Youzan Coder
Youzan Coder
Apr 25, 2025 · Artificial Intelligence

AI-Powered Code Review System: Design, Implementation, and Lessons Learned

The team built a low‑cost AI‑powered code‑review assistant that injects line‑level comments into GitLab merge requests, using LLMs via Feishu, iterating quickly through MVP and optimization phases, achieving 64 integrations, 150+ daily comments, feedback‑driven prompt refinement, and demonstrating high ROI for small‑to‑medium teams while outlining future IDE and rule‑based extensions.

AIAutomationGitLab
0 likes · 17 min read
AI-Powered Code Review System: Design, Implementation, and Lessons Learned
Eric Tech Circle
Eric Tech Circle
Apr 25, 2025 · Artificial Intelligence

How AI‑Powered Cursor Turns Text Prompts into Precise PlantUML Diagrams

This article shows how the Cursor IDE’s built‑in AI can generate complete PlantUML code for various system diagrams—from RBAC models and login flows to payment processes, DDD layering, and C4 architecture—dramatically cutting manual drawing time and keeping documentation in sync with code.

AICursor IDEPlantUML
0 likes · 17 min read
How AI‑Powered Cursor Turns Text Prompts into Precise PlantUML Diagrams
Fun with Large Models
Fun with Large Models
Apr 25, 2025 · Artificial Intelligence

Why Your RAG System Underperforms and How to Boost Its Effectiveness by 20%

This article analyzes common shortcomings of RAG pipelines—data preparation, retrieval, and LLM generation—and provides concrete optimization techniques such as advanced chunking, embedding model selection, retrieval parameter tuning, rerank models, and prompt engineering, promising up to a 20% performance gain.

ChunkingEmbeddingPrompt engineering
0 likes · 17 min read
Why Your RAG System Underperforms and How to Boost Its Effectiveness by 20%
Nightwalker Tech
Nightwalker Tech
Apr 21, 2025 · Artificial Intelligence

Turning AI into a Reliable Engineering Partner: Methodology, Rules, and Practices

This article outlines a comprehensive methodology for integrating AI—particularly large language models—into software development workflows by establishing knowledge‑base templates, rule systems, multi‑model collaboration, context management, and task decomposition to transform AI from a whimsical code generator into a trustworthy engineering partner.

AIAutomationLLM
0 likes · 16 min read
Turning AI into a Reliable Engineering Partner: Methodology, Rules, and Practices
Smart Era Software Development
Smart Era Software Development
Apr 19, 2025 · Artificial Intelligence

How to Build Robust LLM Agents with OpenAI’s Open‑Source Guide

This guide walks developers through when to use LLM agents, the three‑component design (model, tools, instructions), model selection, tool definition, prompt best practices, orchestration patterns (single, manager, decentralized), guardrails, and human‑in‑the‑loop, all illustrated with OpenAI Agents SDK code examples.

Agents SDKGuardrailsLLM Agents
0 likes · 22 min read
How to Build Robust LLM Agents with OpenAI’s Open‑Source Guide
DevOps
DevOps
Apr 17, 2025 · Artificial Intelligence

Building a Google Prompt‑Engineering Assistant with Coze

This guide explains how to use Google’s Prompt‑Engineering Whitepaper to create a Coze knowledge‑base and workflow that can answer prompt‑engineering questions, generate high‑quality prompts, and demonstrate practical AI prompt‑crafting techniques for users.

AICozeGoogle Whitepaper
0 likes · 6 min read
Building a Google Prompt‑Engineering Assistant with Coze
AntTech
AntTech
Apr 11, 2025 · Artificial Intelligence

Understanding MCP and Function Call: A Comprehensive Guide to LLM Tool Integration

This article explains the MCP protocol and Function Call mechanism for large language models, detailing how tools are described, invoked, and processed, and provides practical code examples ranging from OpenAI JSON specifications to fast‑MCP Python and Spring MVC implementations.

AI tool integrationLarge Language ModelMCP
0 likes · 14 min read
Understanding MCP and Function Call: A Comprehensive Guide to LLM Tool Integration
Open Source Linux
Open Source Linux
Apr 8, 2025 · Artificial Intelligence

A Turing‑Award Legend on AI, Parallel Computing, and Learning's Future

In this candid interview, 83‑year‑old Turing‑Award winner Jeffrey Ullman reflects on his decades‑long impact on compilers, databases, and algorithms, discusses the unpredictable nature of technological revolutions, explores the rise of large language models, parallel computing, prompt engineering, and the challenges of adapting education and software engineering to rapid AI‑driven change.

Education TechnologyPrompt engineeringartificial-intelligence
0 likes · 23 min read
A Turing‑Award Legend on AI, Parallel Computing, and Learning's Future
Beijing SF i-TECH City Technology Team
Beijing SF i-TECH City Technology Team
Apr 7, 2025 · Artificial Intelligence

LLM Application in Text Information Detection and Extraction: A Case Study of Blue-Collar Recruitment Data Processing

This article explores the application of Large Language Models (LLM) in text information detection and extraction, focusing on blue-collar recruitment data processing. It details the implementation of LLM through prompt engineering, RAG enhancement, and model fine-tuning to improve data cleaning efficiency and accuracy.

AI ApplicationsLLMPrompt engineering
0 likes · 31 min read
LLM Application in Text Information Detection and Extraction: A Case Study of Blue-Collar Recruitment Data Processing
JD Cloud Developers
JD Cloud Developers
Apr 7, 2025 · Artificial Intelligence

Why Bigger Prompts Fail: Modular Strategies for Building Efficient AI Agents

This article explains why overloading prompts and tools harms AI‑Agent performance, and offers practical modular design, intent‑driven instruction splitting, and efficient context management strategies such as curated function‑call tools and dynamic RAG to reduce token costs, improve response speed, and avoid hallucinations.

AI AgentLLMPrompt engineering
0 likes · 13 min read
Why Bigger Prompts Fail: Modular Strategies for Building Efficient AI Agents
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Apr 7, 2025 · Frontend Development

How to Build Frontend Components Faster in the AI Era?

The article reviews 21st.dev, an open‑source React UI component marketplace inspired by shadcn/ui, highlighting its atomic "code‑out" installation, AI‑friendly prompts, MCP service, and step‑by‑step usage that enable zero‑code component generation in minutes, while comparing it with traditional npm workflows and discussing its strengths, limitations, and broader implications for private component libraries.

AIMCPPrompt engineering
0 likes · 13 min read
How to Build Frontend Components Faster in the AI Era?
Ops Development & AI Practice
Ops Development & AI Practice
Apr 5, 2025 · Artificial Intelligence

Why Do LLMs Follow Instructions So Well? Unpacking the Secrets

This article explains the concept of instruction‑following in large language models, compares early and modern LLMs, details the training techniques that enable it, highlights its importance, offers practical prompting tips, and discusses current challenges and future directions.

AILLMPrompt engineering
0 likes · 10 min read
Why Do LLMs Follow Instructions So Well? Unpacking the Secrets
DeWu Technology
DeWu Technology
Apr 2, 2025 · Frontend Development

Enhancing Front-End Development with Cursor AI: Workflow, Planning, and Impact Assessment

The article explains how integrating the Cursor AI assistant into front‑end development reshapes workflow by separating planning from execution, using iterative context loops for analysis, design, and impact assessment, guiding minimal‑change code generation and testing, and ultimately shifting developer skill from memorizing APIs to asking precise questions.

AI‑assisted developmentPrompt engineeringcode generation
0 likes · 13 min read
Enhancing Front-End Development with Cursor AI: Workflow, Planning, and Impact Assessment
Huolala Tech
Huolala Tech
Apr 1, 2025 · Frontend Development

How Frontend Teams Can Leverage LLMs for Real‑Time Compliance Checks

This article explains how frontend developers can use large language models to detect and prevent marketing content violations in WeChat mini‑programs, covering pain‑point discovery, LLM‑driven compliance architecture, prompt optimization, model selection, testing methods, and seamless frontend integration with Feishu notifications.

AILLMPrompt engineering
0 likes · 10 min read
How Frontend Teams Can Leverage LLMs for Real‑Time Compliance Checks
Tencent Cloud Developer
Tencent Cloud Developer
Apr 1, 2025 · Artificial Intelligence

AI‑Assisted Code Refactoring for Go Projects: A Step‑by‑Step Guide

By following a seven‑step workflow—scanning Go code, discussing refactoring plans, creating structured Cursor rules, optionally deep‑diving into complex logic, iteratively applying rewrites with comments and tests, running AI self‑review, updating documentation, and performing full verification—developers combine AI speed with human judgment to efficiently refactor projects and reduce technical debt.

AI code refactoringGo ProgrammingPrompt engineering
0 likes · 13 min read
AI‑Assisted Code Refactoring for Go Projects: A Step‑by‑Step Guide
Cognitive Technology Team
Cognitive Technology Team
Mar 30, 2025 · Artificial Intelligence

Why Prompt Engineering Is the “Mind‑Reading” Technique of AI: The Crucial Role of In‑Context Learning

Prompt engineering uses in‑context learning to turn large language models into precise, task‑aware assistants by providing well‑crafted prompts that guide the model’s probability distribution, reduce hallucinations, and unlock hidden knowledge without any parameter tuning.

In-Context LearningPrompt engineeringartificial-intelligence
0 likes · 6 min read
Why Prompt Engineering Is the “Mind‑Reading” Technique of AI: The Crucial Role of In‑Context Learning
Architect
Architect
Mar 29, 2025 · Artificial Intelligence

How Non‑AI Developers Can Build Powerful LLM Apps: Prompt Engineering, RAG, and AI Agents Explained

This article guides developers without an AI background through the fundamentals of building large‑language‑model applications, covering prompt engineering, multi‑turn interaction, function calling, retrieval‑augmented generation, vector databases, code assistants, and the MCP protocol for AI agents.

AI AgentEmbeddingFunction Calling
0 likes · 51 min read
How Non‑AI Developers Can Build Powerful LLM Apps: Prompt Engineering, RAG, and AI Agents Explained
Qborfy AI
Qborfy AI
Mar 29, 2025 · Artificial Intelligence

Mastering LangChain: Build LLM Apps with Chains, Agents, and Vector Stores

This tutorial walks through the limitations of simple prompt usage, introduces LangChain as a framework for building full‑featured LLM applications, explains its core concepts and components, and provides step‑by‑step code examples for installing, configuring, and running a basic LangChain demo.

AI ApplicationAgentsLLM
0 likes · 11 min read
Mastering LangChain: Build LLM Apps with Chains, Agents, and Vector Stores
Qborfy AI
Qborfy AI
Mar 28, 2025 · Artificial Intelligence

Master Prompt Engineering: From Basics to Advanced SQL Generation

This article walks readers through the fundamentals of prompt engineering—covering role, context, instruction, examples, and output formatting—then demonstrates a step‑by‑step construction of a sophisticated SQL‑generation prompt, complete with concrete code snippets, best‑practice tips, and reference resources.

AI Prompt DesignInstruction TuningPractical Examples
0 likes · 21 min read
Master Prompt Engineering: From Basics to Advanced SQL Generation
Architect
Architect
Mar 27, 2025 · Artificial Intelligence

How to Use Anthropic’s Model Context Protocol for Seamless LLM Integration

This article explains Anthropic’s open‑source Model Context Protocol (MCP), its client‑server architecture, resource and tool definitions, sampling workflow, and provides step‑by‑step Python examples for building a PoE2 hot‑fix fetcher and a simple chatbot that leverages MCP to connect large language models with external data sources and functions.

AI toolsLLM integrationMCP
0 likes · 14 min read
How to Use Anthropic’s Model Context Protocol for Seamless LLM Integration
Radish, Keep Going!
Radish, Keep Going!
Mar 26, 2025 · Artificial Intelligence

Is AI Turning Software Engineers into Managers? Exploring the Identity Crisis

The article examines how AI coding assistants are reshaping software engineers' roles—from hands‑on creators to overseers—triggering an identity crisis, while exploring the loss of coding joy, emerging practices like prompt engineering, and strategies to adapt and retain core engineering craftsmanship.

AI coding assistantsPrompt engineeringcareer evolution
0 likes · 23 min read
Is AI Turning Software Engineers into Managers? Exploring the Identity Crisis
DaTaobao Tech
DaTaobao Tech
Mar 26, 2025 · Artificial Intelligence

Overview of Retrieval-Augmented Generation (RAG) and Related AI Technologies

The article surveys Retrieval‑Augmented Generation (RAG) as a solution to large language model limits—such as outdated knowledge, hallucinations, and security risks—by integrating vector‑database retrieval with LLM generation, and discusses related tools, multi‑agent frameworks, prompt engineering, fine‑tuning methods, and emerging optimization trends.

AI ApplicationsLLMMulti-Agent Systems
0 likes · 29 min read
Overview of Retrieval-Augmented Generation (RAG) and Related AI Technologies
DeWu Technology
DeWu Technology
Mar 24, 2025 · Artificial Intelligence

Understanding Multi‑Agent AI Systems: ReAct Architecture, MCP Protocol, and OpenManus Implementation

Understanding multi‑agent AI systems, this article explains how ReAct’s tightly coupled reasoning‑action loop, the Model Context Protocol, and the open‑source OpenManus implementation enable autonomous task planning, tool invocation, and memory management, contrasting traditional chatbots with delivery‑centered agents while highlighting current limitations and future optimization needs.

AI agentsMCPOpenManus
0 likes · 24 min read
Understanding Multi‑Agent AI Systems: ReAct Architecture, MCP Protocol, and OpenManus Implementation
AI Frontier Lectures
AI Frontier Lectures
Mar 21, 2025 · Artificial Intelligence

Can Chain‑of‑Thought Templates Unlock Higher Reasoning Limits in LLMs?

The article examines how chain‑of‑thought (CoT) templates are evolving from short‑term heuristics to long‑range planning in large language models, highlighting recent advances such as OpenAI o1, DeepSeek R1, and Kimi 1.5, and explores template designs that boost reasoning performance, efficiency, and multimodal capabilities.

AI reasoningChain-of-ThoughtEfficiency
0 likes · 7 min read
Can Chain‑of‑Thought Templates Unlock Higher Reasoning Limits in LLMs?
Java Tech Enthusiast
Java Tech Enthusiast
Mar 21, 2025 · Artificial Intelligence

Techniques for Removing AI-Generated Text Signatures

The article outlines how AI‑generated essays often exhibit overly structured sections, repetitive phrasing, and mechanical punctuation, and then provides practical tactics—such as de‑structuring transitions, inserting personal anecdotes, swapping hot words, using irregular punctuation, adding examples, and de‑professionalizing tone—to mask these signatures and lower plagiarism detector scores.

AI writingPrompt engineeringText Editing
0 likes · 8 min read
Techniques for Removing AI-Generated Text Signatures
Eric Tech Circle
Eric Tech Circle
Mar 21, 2025 · Operations

Boost Your Git Workflow with AI: 5 Practical Scenarios Using Cursor

This guide demonstrates how to integrate the Cursor AI editor with Git to automate commit message generation, streamline branch management, perform intelligent code reviews, resolve merge conflicts, and generate commands, offering concrete prompts, shortcuts, and visual examples that enhance version‑control efficiency for development teams.

AI integrationCursorGit
0 likes · 8 min read
Boost Your Git Workflow with AI: 5 Practical Scenarios Using Cursor
IT Services Circle
IT Services Circle
Mar 20, 2025 · Artificial Intelligence

How to Remove the AI‑Generated Signature from Your Writing

This guide explains the typical characteristics of AI‑generated text and provides practical, step‑by‑step techniques—such as de‑structuring, logic reshaping, keyword substitution, punctuation tweaking, adding concrete examples, and using informal analogies—to make AI‑written content appear more human‑like and reduce detection risk.

AI writingPrompt engineeringText Editing
0 likes · 9 min read
How to Remove the AI‑Generated Signature from Your Writing
Sohu Tech Products
Sohu Tech Products
Mar 19, 2025 · Artificial Intelligence

How to Recreate a Translation Agent with LangGraph and LLMs

This guide demonstrates building a steerable LLM‑based translation workflow using LangGraph, covering the initial translation, model‑generated reflection suggestions, and final improvement steps with full Python code examples and a complete execution result.

AILLMLangGraph
0 likes · 34 min read
How to Recreate a Translation Agent with LangGraph and LLMs
Sohu Tech Products
Sohu Tech Products
Mar 19, 2025 · Artificial Intelligence

Easy DataSet: An Open‑Source Tool for Building Domain‑Specific Datasets and Fine‑Tuning Large Language Models

The article introduces Easy DataSet, an open‑source tool that streamlines the creation of domain‑specific datasets by aggregating public data sources, chunking Markdown documents, generating and managing QA pairs with configurable LLM endpoints, and exporting them in common formats, while outlining its architecture and future roadmap.

AIData ManagementLLM fine-tuning
0 likes · 30 min read
Easy DataSet: An Open‑Source Tool for Building Domain‑Specific Datasets and Fine‑Tuning Large Language Models
DaTaobao Tech
DaTaobao Tech
Mar 14, 2025 · Artificial Intelligence

AI-Driven Engineering Efficiency: Practices and Insights from a Live-Streaming Team

The article recounts a live‑streaming team’s six‑month experiment using large‑language‑model AI to boost backend, frontend, testing, data‑science and data‑engineering productivity, detailing goals, LLM strengths and limits, and practical tactics such as task splitting, input refinement, human‑AI guidance, retrieval‑augmented generation and fine‑tuning, while emphasizing disciplined task design, prompt iteration, and future vertical integrations.

AIPrompt engineeringRAG
0 likes · 17 min read
AI-Driven Engineering Efficiency: Practices and Insights from a Live-Streaming Team
58 Tech
58 Tech
Mar 11, 2025 · Artificial Intelligence

Applying Large Language Models to Real Estate Recommendation: Case Studies and Optimization Techniques

This article presents a comprehensive case study on how large language models are integrated into 58.com’s real‑estate recommendation platform, detailing challenges, data adaptation, prompt and parameter optimizations, embedding generation, conversational recommendation, and future directions for multimodal and generative recommendation systems.

EmbeddingPrompt engineeringRecommendation Systems
0 likes · 14 min read
Applying Large Language Models to Real Estate Recommendation: Case Studies and Optimization Techniques
Tencent Technical Engineering
Tencent Technical Engineering
Mar 10, 2025 · Artificial Intelligence

How Non‑AI Developers Can Build LLM Apps: Prompt Engineering, RAG, and Function Calling Explained

This guide shows non‑AI developers how to create large‑model applications by mastering prompt engineering, multi‑turn interactions, Retrieval‑Augmented Generation, function calling, and AI‑Agent integration, with practical code examples, tool design patterns, and deployment tips.

AI AgentEmbeddingFunction Calling
0 likes · 48 min read
How Non‑AI Developers Can Build LLM Apps: Prompt Engineering, RAG, and Function Calling Explained
CSS Magic
CSS Magic
Mar 10, 2025 · Artificial Intelligence

Three Advanced Ways to Harness DeepSeek for Everyone

The article outlines three practical approaches to get the most out of DeepSeek—using it as a conversational assistant, integrating its API to power AI tools such as the Chrome immersive‑translation plugin, and leveraging it for AI‑assisted programming—while comparing the V3 and R1 models and offering concrete configuration steps.

AI programmingAI translationAPI integration
0 likes · 8 min read
Three Advanced Ways to Harness DeepSeek for Everyone
DevOps
DevOps
Mar 9, 2025 · Artificial Intelligence

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

This article provides a comprehensive introduction to developing large language model (LLM) applications, covering prompt engineering, zero‑ and few‑shot techniques, function calling, retrieval‑augmented generation (RAG) with embedding and vector databases, code assistants, and the MCP protocol for building AI agents, all aimed at non‑AI specialists.

AI AgentEmbeddingFunction Calling
0 likes · 48 min read
A Beginner's Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling, and AI Agents
Fun with Large Models
Fun with Large Models
Mar 9, 2025 · Artificial Intelligence

Is Manus’s $10,000 Invite a Tech Revolution or a Patchwork AI Hype? In‑Depth Review

The article examines the hype around Manus, an AI agent whose invitation codes sell for up to $10,000, by dissecting its interface, testing long‑text generation, price‑comparison, and financial‑analysis tasks, revealing reliance on existing tools, hallucination errors, high token costs, and offering open‑source alternatives.

ManusOpen-source alternativesPrompt engineering
0 likes · 13 min read
Is Manus’s $10,000 Invite a Tech Revolution or a Patchwork AI Hype? In‑Depth Review
Code Mala Tang
Code Mala Tang
Mar 8, 2025 · Artificial Intelligence

14 Powerful Prompt Engineering Techniques to Unlock AI’s Full Potential

This article introduces the fundamentals of prompt engineering and presents fourteen practical techniques—ranging from role‑playing and step‑by‑step reasoning to chain‑of‑thought and ReAct—that help users craft precise, high‑quality prompts for any large language model, dramatically improving AI output.

AIAI productivityLLM techniques
0 likes · 16 min read
14 Powerful Prompt Engineering Techniques to Unlock AI’s Full Potential
dbaplus Community
dbaplus Community
Mar 7, 2025 · Artificial Intelligence

Master Prompt Engineering: Frameworks, Strategies, and Real‑World Examples for Large Language Models

This comprehensive guide explains what prompts are, outlines essential prompt components and multiple engineering frameworks, presents practical strategies for crafting clear and structured prompts, addresses model limitations such as hallucinations, and showcases a wide range of advanced prompting techniques with code examples.

AILLMPrompt engineering
0 likes · 29 min read
Master Prompt Engineering: Frameworks, Strategies, and Real‑World Examples for Large Language Models
Fun with Large Models
Fun with Large Models
Mar 6, 2025 · Artificial Intelligence

Master Prompt Engineering: Make AI Follow Your Commands with Simple, Effective Prompts

Prompt engineering transforms vague queries into precise, reliable AI responses by structuring prompts with clear instructions, context, input, and output specifications, and by using role‑playing and formatting tricks, enabling models like DeepSeek and OpenAI to deliver accurate, consistent results across tasks.

AI Prompt DesignDeepSeekOpenAI
0 likes · 15 min read
Master Prompt Engineering: Make AI Follow Your Commands with Simple, Effective Prompts
Open Source Linux
Open Source Linux
Mar 5, 2025 · Artificial Intelligence

How DeepSeek‑R1 Redefines Prompt Engineering and Real‑World AI Deployment

The article analyzes DeepSeek‑R1’s low‑cost inference architecture, Chinese language optimizations, novel prompt‑engineering techniques, and the practical challenges of deploying large domestic models, offering insights into vertical AI applications and the evolving open‑source ecosystem in China.

AI DeploymentDeepSeekLarge Language Model
0 likes · 8 min read
How DeepSeek‑R1 Redefines Prompt Engineering and Real‑World AI Deployment
Code Mala Tang
Code Mala Tang
Mar 3, 2025 · Artificial Intelligence

Unlock AI’s Full Potential with Structured Prompt Decorators

Prompt Decorators are structured prefixes that standardize and enhance AI responses, addressing common challenges like vague prompts, inconsistent answers, and lack of reasoning by guiding the model to produce clear, logical, and well‑organized outputs across various use cases.

AIAutomationLLM
0 likes · 23 min read
Unlock AI’s Full Potential with Structured Prompt Decorators
Eric Tech Circle
Eric Tech Circle
Mar 3, 2025 · Frontend Development

Auto‑Generate Complete UI Prototypes with Claude 3.7 and Cursor

This guide shows how a full‑stack engineer can leverage Claude 3.7 Sonnet together with the Cursor AI editor to automatically create a full set of UX mock‑ups and HTML code for a Pilates fitness app, using prompt engineering, Ask mode, and step‑by‑step code aggregation.

Claude 3.7Cursor AIHTML generation
0 likes · 4 min read
Auto‑Generate Complete UI Prototypes with Claude 3.7 and Cursor
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Mar 1, 2025 · Fundamentals

How Two Prompts Enable Cursor to Batch‑Generate Unit Tests

The article details a step‑by‑step workflow that uses two carefully crafted prompts with Cursor to automatically locate source files in a large monorepo, record tasks, iteratively generate Vitest unit tests, track progress, and handle failures, turning a 11 k‑line codebase into a semi‑automated test suite.

CursorLLM AutomationPrompt engineering
0 likes · 8 min read
How Two Prompts Enable Cursor to Batch‑Generate Unit Tests
ITPUB
ITPUB
Mar 1, 2025 · Artificial Intelligence

Can DeepSeek AI Replace Your DBA? Real-World Database Scenarios Tested

This article examines DeepSeek, a Chinese AGI‑focused AI model, explains prompt‑engineering techniques, and evaluates its performance across database architecture, development, and operations tasks through concrete Q&A examples, SQL plan analysis, and shell‑script generation, while also discussing its broader impact on professionals, vendors and enterprises.

AIDeepSeekPrompt engineering
0 likes · 10 min read
Can DeepSeek AI Replace Your DBA? Real-World Database Scenarios Tested
Ops Development & AI Practice
Ops Development & AI Practice
Feb 25, 2025 · Artificial Intelligence

What Is Hybrid Reasoning in Claude 3.7 Sonnet and Why It Matters

Hybrid reasoning lets Claude 3.7 Sonnet dynamically switch between fast, intuition‑like answers and step‑by‑step, deep analysis, improving both speed and accuracy for tasks ranging from simple code snippets to complex algorithm design, and signals a broader shift in large language model capabilities.

AI reasoningClaude 3.7Hybrid Reasoning
0 likes · 9 min read
What Is Hybrid Reasoning in Claude 3.7 Sonnet and Why It Matters
phodal
phodal
Feb 24, 2025 · Artificial Intelligence

AI Coding Tools 2.0: Trends, Design Insights, and the AutoDev Sketch Breakthrough

This article analyzes the evolution of AI‑assisted coding tools toward a 2.0 generation, outlines key trends such as agent‑driven architecture, developer‑first experience, and automated validation, and details the design and implementation of the AutoDev Sketch prototype that integrates high‑quality context, prompt engineering, and IDE‑native plugins.

AI codingAutoDevIDE
0 likes · 10 min read
AI Coding Tools 2.0: Trends, Design Insights, and the AutoDev Sketch Breakthrough
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Feb 17, 2025 · Artificial Intelligence

24 Proven Prompt Formulas to Unlock DeepSeek’s Full Potential

Discover a comprehensive collection of 24 structured prompting techniques—from basic role‑play formulas to advanced cross‑disciplinary and managerial frameworks—designed to help users of DeepSeek and other large language models craft precise, high‑impact queries that dramatically improve response quality and efficiency.

AI promptingDeepSeekPrompt engineering
0 likes · 12 min read
24 Proven Prompt Formulas to Unlock DeepSeek’s Full Potential
Ma Wei Says
Ma Wei Says
Feb 13, 2025 · Artificial Intelligence

Master AI Prompting: 5 Proven Techniques to Unlock Accurate Outputs

This guide presents five practical prompting techniques—including structured output, role‑playing, visual conversion, multi‑turn refinement, and multilingual handling—plus industry‑specific examples and common pitfalls, helping users craft precise commands for AI models like DeepSeek.

AI promptingPrompt engineeringlarge language models
0 likes · 8 min read
Master AI Prompting: 5 Proven Techniques to Unlock Accurate Outputs
DevOps
DevOps
Feb 12, 2025 · Artificial Intelligence

A Comprehensive Guide to Prompt Engineering, RAG, and Optimization Techniques for Large Language Models

This article presents a systematic framework for crafting effective prompts, detailing the universal prompt template, role definition, task decomposition, RAG integration, few‑shot examples, memory handling, and parameter tuning to enhance large language model performance across diverse applications.

Prompt engineeringPrompt templatesRAG
0 likes · 24 min read
A Comprehensive Guide to Prompt Engineering, RAG, and Optimization Techniques for Large Language Models