Three core operations to improve job search efficiency
OpenJobs AI compresses the traditional job search process into three key steps through intelligent conversational technology:
- Precise demand input:Combine key elements in natural language in the chat box, e.g. '5 years experience Java developer in Beijing, expected salary 300,000-400,000, experience in financial projects'. The system will confirm the details by asking follow-up questions (e.g. 'Do you need to include provident fund?')
- Dynamic screening techniques:When the first recommendation is unsatisfactory, use the 『Exclude/Prioritize』 command to adjust the result, such as 『Exclude Outsourcing Companies』 or 『Prioritize Displaying Semiconductor Industry』. The matching accuracy of the system can be improved by 60% after each adjustment.
- Real-time tracking feature:If you are satisfied with the position but not suitable for the time being, you can enter "Save this HR contact information", and the relevant data will be automatically stored in the space associated with the account (registration and login are required).
Typical efficiency comparison: traditional platforms need to screen an average of 237 jobs to get 1 interview, while OpenJobs AI users reach an average of 8.3 conversations
This answer comes from the articleOpenJobs AI: Talk to AI to Quickly Match Suitable Jobs》
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