Automatically analyze users' ambiguous or lengthy questions, extract the core intention and key entities, and convert them into structured query instructions. Support referential resolution and context completion in multi-round conversations to ensure the system accurately understands the requirements and significantly reduces retrieval deviations caused by unclear expressions.

Establish a strict "citation traceability" mechanism, forcing the model to answer based on the real document fragments retrieved. For questions that cannot be found with evidence in the knowledge base, the system actively declares "unknown" instead of fabricating facts, and displays the original source to ensure the output content is true, credible, and verifiable.

Intelligently identify document structure, and divide long texts into appropriate knowledge fragments according to chapters, paragraphs, or semantic boundaries. Retain context association and metadata to avoid information fragmentation, ensuring that large models can accurately capture details when processing, and improving the granularity and accuracy of knowledge recall.

Convert enterprise private documents into high-dimensional vectors for storage, and conduct retrieval based on semantic similarity rather than keyword matching. Ensure accurate positioning of relevant information within massive internal data, guarantee data does not exceed the domain, and provide an exclusive, secure, and highly relevant knowledge base for AI responses.

A large manufacturing company has collaborated with CQianzhi Anshi AI to implement the "Xiao Yue Zhi Dai" AWS cloud deployment project. This project aims to address the challenges faced by the company, such as low internal consultation efficiency, high customer service costs, difficult knowledge management, and data privacy risks. It creates an exclusive intelligent question-answering assistant tailored for the enterprise, covering all scenarios of internal services including administration, finance, and product inquiries.

The project is built based on the AWS cloud ecosystem, supporting local knowledge vector retrieval and intent recognition, and avoiding the problem of large model hallucination. After the project is implemented, the efficiency of enterprise knowledge question answering has increased by 200%, the accuracy rate of responses has reached 98%, the cost of manual consultation has decreased by 60%, and employees can complete knowledge base training within 10 seconds without the intervention of technical personnel. This has enabled efficient flow of enterprise knowledge and the upgrade of intelligent services, making it a typical practice of enterprise-level intelligent question-answering cloud deployment in the manufacturing industry.