#zero-shot prompting
16 related articles
·3 min
Stop Writing Prompts by Hand: Let AI Agents Prompt Themselves
Deep dive into the AI coding paradigm shift: from hand-crafted prompts to self-prompting agent loops. Learn how agent self-review and proactive context fetching enable scalable, high-quality AI coding.
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·3 min
The Complete Guide to Prompt Engineering: From Zero to Practical Application
A complete guide to learning Prompt Engineering, covering LLM selection, prompt writing techniques, zero-shot/few-shot prompting, chain of thought reasoning, and Python API development.
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·4 min
Spring AI vs LangChain4j: How to Choose a Java AI Framework
In-depth comparison of Spring AI and LangChain4j — two major Java AI frameworks — covering core features, completeness, ecosystem support, and usability to help Java developers make the right choice.
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·3 min
Claude Code Skills Explained: A Practical Guide to AI-Powered Test Case Generation
Deep dive into Claude Code Skills: four core advantages including progressive loading, version control, and reusability, plus a practical guide to AI-powered test case generation.
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·3 min
Testing DeepSeek's Safety Mechanisms: Multiple Jailbreak Attempts Successfully Blocked
An overseas security blogger systematically tested DeepSeek's jailbreak resistance using direct requests, rephrased prompts, and varied strategies. Results show robust intent recognition, consistent blocking, and context-aware safety mechanisms.
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·4 min
AI + Java Backend Learning Roadmap: Four Stages from CRUD to Senior AI Engineer
A complete AI + Java backend learning roadmap based on Spring AI Alibaba: from prompt engineering and LLM API integration to RAG knowledge bases and Agent systems across four stages.
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·3 min
AI Agent Hands-On Learning Path: A Complete Guide from Beginner to Enterprise-Level Development
A systematic AI Agent development learning roadmap covering prompt engineering, RAG, multi-Agent collaboration, tool calling, and more—with phased learning advice and 28 hands-on project references.
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·3 min
Beginner's Guide to Agent Skills: Structure Breakdown & Custom AI Skill Development
A deep dive into Agent Skill's core concepts and internal structure, covering skill.md, references, scripts, and assets with a restaurant poster Skill example.
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·3 min
AI Large Language Model Learning Roadmap: A Systematic Path from Zero to Project Implementation
A detailed AI LLM learning roadmap covering Transformer architecture, Prompt Engineering, RAG, Agent development, model fine-tuning & deployment, with enterprise project guides.
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·3 min
AI Large Model Learning Roadmap Breakdown: Three Stages from Application Development to Model Fine-Tuning
Deep breakdown of a popular AI large model learning roadmap covering LangChain, RAG, Agent, and LoRA fine-tuning across three stages, with analysis of its strengths and limitations for career changers.
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·4 min
Complete AI Large Language Model Learning Roadmap: From Beginner to Production-Ready
A systematic guide to learning AI large language models, covering Transformer architecture, prompt engineering, RAG, AI Agents, fine-tuning, and enterprise projects from beginner to production-ready.
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·2 min
GitHub Copilot Switches to Per-Token Billing: The End of AI Coding Tool Subsidies
GitHub Copilot shifts from flat monthly fees to per-token billing, potentially costing developers hundreds per day. Analysis of the change, industry trends, and open-source alternatives like Cline and Codex CLI.
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·3 min
AI Large Language Model Learning Path: A Three-Step Practical Guide from Zero to Freelancing
A three-step guide to LLM app development: from Prompt Engineering and API calls, to RAG knowledge bases, to Agent development and multi-agent collaboration.
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Industry Insights·2 min
AI Product Development in Practice: Model Selection, Building Moats, and Paths to Commercialization
Practical strategies for AI product development: why not to train models from scratch, when to use APIs vs. fine-tuning, building product moats, and the full path from evaluation systems to commercialization.
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Tutorials·2 min
Complete Breakdown of Andrew Ng's LangChain Course: Five Core Components and Hands-On Guide
Deep dive into Andrew Ng and Harrison Chase's LangChain course, covering the five core components—Models, Prompts, Indexes, Chains, and Agents—to help developers master LLM app development.
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Deep Dives·3 min
Harness Engineering Explained: Building Stable and Efficient Work Systems for AI
Deep dive into Harness Engineering: how to build execution environments, toolchains, and feedback loops for AI. From Prompt Engineering to system-level engineering for stable AI production.
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