它将从业者经验和主要来源（Anthropic、OpenAI、OWASP等）综合成实用、结构化的手册，包含检查清单、反模式和故障处理表，不同于零散的博客文章或学术论文。
A comprehensive reference for engineers, tech leads, and teams who build AI systems: from choosing an approach, through core techniques (prompting, RAG, fine-tuning, agents), to production engineering, data operations, specialized systems, and governance.
This guide started from practitioner discussions (Reddit engineering threads) and was validated and expanded against primary sources: Anthropic and OpenAI engineering guides, the OWASP Top 10 for LLM Applications (2025), the 12-Factor Agents methodology, OpenTelemetry GenAI conventions, academic work on retrieval and long context, and current regulation (EU AI Act as amended by the 2026 Digital Omnibus). Full references at the end.
Principle: treat AI systems as probabilistic distributed systems, not as ordinary API integrations. Design for containment and recovery, not for a model that never fails.
How to use this document: each section stands alone. Skim the contents, read the sections relevant to your current problem, and use the appendices (failure handling, checklist, anti-patterns, metrics) as working documents during design reviews and launches.
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