AI business demand research work ideas & AI big model related technical architecture diagram

Demand side

1. Three principles of demand exploration :

• All requirements have a purpose, and the "why" must be communicated to the proposer;

• All goals have boundaries, and you need to be clear about your own position;

• All boundaries have implementation paths, and we need to find the best practice paths.

1. Demand processing flow :

• Demand scenario-based : Taking "generating charts through natural language" as an example, abstract requirements are transformed into specific scenarios.

• Path to achieve the requirements : It is broken down into "get accurate data → understand language intent → generate charts", which involves steps such as natural language to SQL, SQL query data, data generation charts, etc., and finally realizes ChatBI.

• Functionalization of requirements : Converting requirements into executable functions (such as ChatBI).

• Demand expansion : Expand from ChatBI to multimodal intelligent BI to enhance application scope and capabilities.

Technical side

1. Tool selection : Provides tools such as Diffy, Milvus/neo4j, and vLLM to support different technology implementations.

1. Required tools : including large model application development platform, vector library/graphic database, and large model operation tools to support technology implementation.

1. Technology stack and theory :

• The core technology stack is based on theories such as the large language model (LLM), vector library, knowledge base and retrieval enhanced generation (RAG). RAG can make up for the limitations of large models in terms of knowledge timeliness and professionalism.

• The technical theory involves large language models, embedding models, time series databases, etc., while focusing on key points such as model fine-tuning, prompt word engineering, data annotation, multimodal technology (such as image and speech models) to ensure the accuracy and effectiveness of technical implementation.

This diagram constructs a complete logic from demand analysis, function implementation to technical support through a two-way mapping of demand and technology, and reflects how to systematically transform user needs into solutions based on a big model.

This “ AI Big Model Related Technology Panorama” adopts a layered design, clearly presenting a complete technical system from the underlying hardware to the upper-level application. The functions of each layer are as follows:

1. Infrastructure layer : Provides basic hardware resources, including GPU/TPU/Ascend (high-performance computing), CPU, memory (RAM), storage (HDD) and network, and is the cornerstone of the entire architecture.

1. Cloud native layer : Use  Docker (containerization technology) and  K8S (container orchestration tool) to achieve efficient deployment, management and elastic expansion of models and applications, ensuring system stability and maintainability.

1. Model layer : includes various core models, such as large language model (LLM), vision-language model, speech-language model, image recognition/OCR model, recall ranking model, intelligent document understanding model and multimodal detection and analysis model, providing diversified intelligent processing capabilities.

1. Application technology layer : covers key technologies for implementing applications, such as  Agent/intelligent agent (autonomous decision-making entity), RAG/retrieval-enhanced generation (combining retrieval and generation), Prompt prompt word engineering (optimizing input instructions), Fine-tuning (model personalized training), COT/thinking chain (step-by-step reasoning), as well as data capture, cleaning, vector processing and access control, to support the implementation of upper-level applications.

1. Application architecture layer : includes engineering technology architecture (technical implementation plan), business architecture (business logic design) and cloud native architecture (cloud environment adaptation design), planning the overall structure and implementation path of the application from a macro perspective.

1. Application layer : presents specific business scenarios, such as  RAG applications (enterprise knowledge base), Agent applications (multi-agent, financial analysis, contract comparison), OLTP applications (intelligent customers, text optimization assistants), OLAP applications (enterprise-level report generation, NLP2SQL BI visualization system), directly serving user needs.

Through layered collaboration, this architecture forms a complete and logically clear technical system from hardware support to specific applications, ensuring the efficient operation and implementation of large AI models in different scenarios.