Introduction
AI Engineering provides a systematic framework for adapting foundation models—such as large language models and multimodal models—to solve real-world problems. The book emphasizes engineering principles and long-term best practices, making it suitable for AI engineers, data scientists, and technical managers.
Key Features
- Covers the end-to-end engineering process for foundation models
- Case-driven, with real-world project experience
- Focuses on model evaluation, prompt engineering, RAG, agent building, and more
- Highlights security, cost, and continuous optimization
Use Cases
- Enterprise AI application development
- AI engineering team process optimization
- Technical management and product planning
- Building AI capability frameworks
Technical Highlights
- Combines traditional ML and foundation model engineering practices
- Systematic coverage of RAG, agent, and prompt engineering
- Adaptable to various scenarios, emphasizing scalability and security