Knowledge Map for Large Model Application Development

Introduction: This article presents a knowledge map for developing large‑model applications, covering architecture, lifecycle, core elements, and case studies.

Technical Architecture: The system is divided into four layers—Infrastructure, Model Tools, Model Engine, and Application—detailing data services, cloud platforms, open‑source models, data construction, training, deployment, routing, and orchestration.

Application Development Lifecycle: It follows the typical stages of requirement definition, solution design, development, and deployment & iteration, emphasizing scenario analysis, model selection, prompt engineering, and performance testing.

Core Elements – Prompt Engineering and Model Fine‑Tuning: The article explains how to craft effective prompts, optimization techniques (few‑shot, step‑by‑step, chain‑of‑thought), and when to resort to fine‑tuning, including PEFT methods such as LoRA.

Evaluation and Integration: Various evaluation methods for general, composite, and business‑specific capabilities are discussed, along with integrating high‑performing models into an atomic capability system and workflow orchestration.

Application Cases: Examples of AI search, assistants, inference middleware, vector retrieval, and generative tools illustrate the breadth of large‑model deployments across industries.