Path
AI engineering practice
An engineering path from prototype to production for large model applications, covering core topics such as Prompt Engineering, RAG Architecture, LLM Ops, and more.
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1. Technical Interpretation Index | Curated Translations
postOriginal technical interpretation and selected articles from foreign technology communities to explore best practices in AI engineering
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2. Original interpretation: Discovery and prevention of silent hallucination in RAG system
postBased on an in-depth analysis of RAG system failure cases in the production environment, we explore the nature of the silent illusion problem, monitoring blind spots, and architectural-level solutions.
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3. Original interpretation: How AI Agent implements large-scale testing quality access control
postPractical analysis of AI testing agent based on Node.js project scaffolding, and explore the implementation ideas of automated quality access control
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4. AI engineering implementation practice map
guideA practical guide to AI engineering around BMAD, Speckit, and spec-driven development.
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5. Original interpretation: Agent quality assessment - the cornerstone of trust in the AI era
postIn-depth analysis of the essential challenges of Agent quality assessment and why quality engineering is the key to determining the success or failure of AI products
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6. Original interpretation: MCP protocol - the USB-C moment of the Agent ecosystem
postAn in-depth analysis of the essence of the Model Context Protocol protocol design and why standardization is the key to the prosperity of the Agent ecosystem
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7. Original Interpretation: Contextual Engineering—The Forgotten Core Battlefield in the AI Era
postAn in-depth analysis of the essential challenges of Agent memory systems and why context management is the key to determining the success or failure of AI products.