Si Xiao Pin---An AI Job Search Assistant

                                Author: Chenhua Liu

Let me revolutionize your job search by utilizing cutting-edge AI technology to uncover hidden opportunities and help you stand out in a competitive job market.

Looking for a job can be a daunting task, especially when you have to sift through countless job listings, tailor your resume and cover letter for each application, and prepare for interviews. As a developer, I recognized the need for an AI-powered solution that could streamline the job search process and help job seekers stand out from the crowd. This is why I developed an AI job search assistant.

What is an AI job search assistant?
An AI job search assistant is an intelligent software application that utilizes machine learning and natural language processing technologies to assist job seekers in the job search process. It offers a variety of features, such as personalized job recommendations, resume and cover letter optimization, interview preparation. The details are as follows:


We first discuss the potential applications of “Si Xiao Pin”, the targeted end users, and their respective needs.

Application scenarios and targeted end users

Application scenarios mainly include but are not limited to the following six:

Targeted end users:


Users choose different functions according to their needs

Provide standard answers to interview questions and score users' responses

Reply to a user's request for help

Ask if the user is satisfied, if not, modify (not demonstrated here)


During the Ask Questions module experiment, we found that if LLM is only instructed to ask interview questions to users, it not only responds differently each time but also tends to ask the same questions repeatedly after responding. In order to improve the performance of the module and prevent such issues, we realized the need to provide additional prompts to limit the LLM's question function. This will help in ensuring a more controlled and streamlined interview process, where questions are asked only as needed, without causing redundancy or repetition.

Through the interaction with LLM, to understand some basic functions of job-hunting function; And understand some process of the interview, in order to limit the function of the simulation mold block; Understand the basic interview scoring criteria.

So, how does it work? Let's break it down.

Product design: The “Si Xiao Pin” app is divided into two modules: the job search assistant module and the mock interview module.

The job search assistant module can be roughly divided into three working units. The first unit is used to extract the profession from the user's input as part of the input for the second unit. The second unit can answer user's questions about job search based on the user's input about job search issues and the profession obtained from the first unit. Then, it will enter the third unit to ask the user if they are satisfied with the response. If not, it will modify the response according to the user's input.
Mock Interview Module: This module consists of four working units. The first unit generates an interview question for the user based on a provided interview scenario. After the user inputs their response, the second unit provides a standard answer based on the interview question generated by the previous unit. Then, the third unit evaluates the user's response based on the interview question, scenario, standard answer, and user input, and provides feedback to the user. Finally, the user's response is scored based on the output of the previous unit.The following is the design drawing of the product:

Fig-1: System Design of Product

What are the product design features of the AI job search assistant?

The product design overall adopts the AI-Chain philosophy, linking the inputs and outputs of multiple working units to make LLM's answers more reasonable. Specifically, the final design philosophy allows each physically separated module to be logically connected to one another, so that each working unit has a certain degree of connection, forming a chain-like structure with close front-to-back connections.


The AI job search assistant was developed using a non-code-friendly AI integrated development environment (IDE) called Sapper. The tool supports code-free programming and drag-and-drop development, and was launched in March 2023. Sapper is like a production line for AI products, helping people develop various AI applications in a short period of time. Users of this IDE only need a general understanding of the expected functionality of the AI tools they want to develop. Using Sapper's unique puzzle-like development model, each required function can be placed in a separate unit of work, along with hints, model selection, and logic, and then logically pieced together to complete the development process. The development process is simple and straightforward. The following figure shows the main parts of Sapper's AI job search assistant:

Fig-1: Worker Design of the Product


As the job market becomes increasingly competitive, job seekers are constantly looking for new ways to stand out and get noticed by potential employers. That's where AI job search assistants come in - powerful tools that leverage the latest advances in machine learning and natural language processing to help job seekers find their dream job.

Over the course of this blog, we have explored various ways in which AI can assist in job searching, from resume optimization and job matching to interview preparation and career coaching. As AI continues to advance, we can expect even more exciting developments in this area.

Thank you for joining me on this journey, and I wish you all the best in your career endeavors.