「朋友成日問我，AI 架構點樣落地？其實唔係淨係砌模型咁簡單，真正嘅挑戰係 scalability, latency 同 integration。」

As an AI Architect, I’ve spent the past few years designing scalable, production-ready AI systems for enterprise use. One of the most common misconceptions I hear is: “Just plug in ChatGPT and you’re done.” But real-world AI architecture involves much more than that.

Here’s a quick breakdown of what I focus on when building AI systems:
🧩 Modular Design: From data ingestion to inference, each component must be decoupled and reusable.
⚙️ MLOps Integration: CI/CD pipelines for models, monitoring, rollback strategies.
🧠 LLM Optimization: Context engineering, RAG pipelines, vector DB tuning.
🌐 Latency Management: Async processing, caching strategies, edge deployment.
🔐 Security & Governance: Data privacy, access control, audit trails.

「AI 唔係魔法，係工程。識得設計，先至可以落地。」
If you’re working on something exciting in the AI space, I’d love to connect. Let’s build smarter systems together.