Case Study: Applying AIGC to Component Activity Business with Dify

This article analyzes the application of AIGC in component activity business, exploring what AI can do in such scenarios and how to quickly validate AI‑business ideas using the Dify low‑code platform.

Background: the component activity system is a mature, self‑service platform that enables product operators to create feature‑complete, secure landing pages without any coding. The creation process consists of five steps: 1) create activity, 2) select components, 3) refine front‑end elements, 4) configure back‑end rules, and 5) generate the landing page.

Pain point: as the component library has grown to over 200 components across 6 major categories and 40 sub‑categories, selecting the right component becomes time‑consuming and difficult.

Solution – Activity Component AI Assistant: a new button on the canvas opens a dialog where users describe their activity requirements in natural language. The AI recommends the most suitable components, provides rationale, and allows one‑click insertion into the canvas, dramatically reducing component selection cost.

Rapid implementation of a business Agent using Dify involves five steps: (1) business data preparation – OCR of component cover images combined with component names, categories, and tags; (2) generate markdown descriptions for each component via an LLM; (3) build a structured JSON dataset containing component identifiers, types, and descriptions; (4) create an embedding‑based knowledge base from the dataset; (5) design a RAG & Agent workflow that matches user prompts to the knowledge base, returns a component list and recommendation reasons.

The Dify workflow follows the chain: Prompt → LLM polishing → Agent → Knowledge base → Business knowledge + Prompt → LLM → Structured output. The platform provides detailed tracing of each step, including execution time, token consumption, and input prompts, facilitating debugging and optimization.

Finally, the workflow’s capabilities are exposed to the business system through Dify’s RESTful API, completing the end‑to‑end AI integration.

Comparison: low‑code platforms like Dify are ideal for quickly validating ideas in scenarios with modest performance and cost constraints, while development frameworks such as LangChain are preferable for highly customized or performance‑sensitive applications. The two approaches can also be combined.

Conclusion: the case study demonstrates how AIGC can be leveraged in component activity business, highlighting the potential of AI to accelerate business innovation when combined with low‑code tools.