Liepin (猎聘)

Solution

Liepin worked with Alibaba Cloud Bailian and Qwen models to insert large-model reasoning into multiple steps of the recruitment chain instead of only adding a chatbot on top of search. The company uses Bailian-based prompt customization to generate and refine job descriptions, then reuses the same model layer in intelligent job posting, person-to-position matching, interview support, and summarization workflows. Because the platform serves enterprises, headhunters, and job seekers in one loop, the model outputs are designed to shorten matching time while keeping recommendations and communications more targeted than simple keyword filters allow. In practice, JD generation, matching, and interview summarization run as linked services on one LLM foundation rather than disconnected tools, which helps Liepin upgrade the recruitment process with shared prompts and model behavior while improving response quality for both recruiters and candidates.

Data flow

Recruiter requirements enter the Bailian application layer, where prompt-optimized Qwen models generate or refine job descriptions, then reuse the same context for intelligent posting, person-to-position matching, and interview-summary generation. Those outputs are returned to recruiters and platform users as part of one connected recruitment workflow.