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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 assistantCode GenerationCursor
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 agentsAutoDevCoding Assistant
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.

AIAutomationCode review
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.

EmbeddingPrompt engineeringRAG
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.

AIAutomationCode Generation
0 likes · 16 min read
Turning AI into a Reliable Engineering Partner: Methodology, Rules, and Practices
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 integrationMCPPrompt engineering
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 developmentCode GenerationPrompt engineering
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.

AIIntegrationLLM
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 ApplicationLLMLangChain
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 engineeringSoftware Engineering
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 applicationsLLMPrompt engineering
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 reasoningLong CoTPrompt engineering
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 integrationCursorDevOps
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.

AIFine-tuningPrompt engineering
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.

AI OptimizationEmbeddingPrompt engineering
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 translationChrome Extension
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
AI Frontier Lectures
AI Frontier Lectures
Mar 6, 2025 · Artificial Intelligence

Can General AI Agents Evolve from Data Gatherers to Professional Deliverables?

The article evaluates the Manus agent’s current strengths in information‑gathering tasks, contrasts collaborative versus fully‑delegated agent models, identifies structural and context limitations that hinder professional‑grade outputs, and speculates on how future agents might bridge this gap.

AIAgent DesignCollaborative AI
0 likes · 5 min read
Can General AI Agents Evolve from Data Gatherers to Professional Deliverables?
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 deploymentDeepSeekModel Optimization
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 automationMonorepo
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 engineeringStructured Output
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.

AI OptimizationFew‑Shot LearningPrompt Templates
0 likes · 24 min read
A Comprehensive Guide to Prompt Engineering, RAG, and Optimization Techniques for Large Language Models
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-ShotMemory
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 productivityDeepSeekPrompt engineering
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 agentsMemory
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.

CoachLMData FlywheelLLMOps
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 AgentData ScienceFunction 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.

LLMPrompt engineeringText-to-SQL
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.

AIDevOpsPrompt 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 AgentDebuggingPrompt engineering
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 DesignFew-ShotPrompt engineering
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 promptingFew‑Shot LearningLLM 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.

AILLMPrompt Design
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 SafetyModel EvaluationPrompt 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 RetrievalPrompt engineering
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 AgentPrompt engineeringSix Thinking Hats
0 likes · 13 min read
How to Build a Six Thinking Hats AI Agent: From Concept to Deployment