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Enhancing Candidate Performance Through Personalized AI Interview Practice
Monster Job Search partnered with us to introduce an AI-powered interview preparation tool, designed to help job candidates practice interview scenarios based on job descriptions. The AI feature provides candidates with personalized feedback, improving their confidence and performance in real-world interviews.
Developing a Strategic AI Solution to Boost Candidate Interview Readiness
As part of our long-standing collaboration with Monster, we partnered to build an AI interview preparation tool featured in the company’s mobile application. The goal was to simulate realistic interview scenarios, allowing job seekers to practice and receive real-time, accurate feedback. Experimenting with different large language models (LLMs), such as GPT-3.5, GPT-4, and later the more cost-effective GPT-4 Turbo, we crafted an AI agent that dynamically adjusted interview questions based on job descriptions and user profiles.
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Scope of Work: Cross-functional Expertise Driving Innovation
This project required a cross-functional setup with competencies in AI research and development to UX/UI design and cost analysis. Throughout the process, continuous experimentation and fine-tuning were critical in aligning the solution with business goals and user needs.
AI interview process design and UX/UI refinement
We created an intuitive user interface that allowed candidates to interact with the AI agent in a simple but effective flow that replicated a real interview experience.
Cost analysis and API usage optimization
A thorough cost-benefit analysis was conducted to keep API usage within budget, ensuring the AI queries were resource-efficient and scalable for production.
Speech-to-text technology integration
We tested and integrated speech-to-text functionality to enhance the interactive experience. This lets users communicate with the AI naturally through voice inputs.
Prompt engineering for quality feedback
By refining prompts on the backend, before they were sent for processing in the model, we could optimize the AI’s ability to generate interview questions and deliver detailed, personalized feedback.
Exploratory Phase and Research-Driven Innovation
The project began with a 3-month ideation phase, where we meticulously explored the potential of integrating AI into the candidate’s recruitment process. This phase was dedicated to testing the capabilities of speech-to-text technologies, prompt engineering, and AI models. Our goal was to make the AI interaction as close to a real interview experience as possible, offering users an immersive and valuable practice tool.
We experimented with multiple AI models, going through GPT-3.5, GPT-4, and later transitioning to GPT-4 Turbo to reduce costs and processing times without compromising quality. Our experiments with different GPT models revealed limitations such as slower response times and suboptimal feedback quality. Although these limitations were mainly due to the capabilities of models, we also refined the system’s logic and optimized the prompts to produce higher-quality responses.
Scope of Work: Cross-functional Expertise Driving Innovation
This project required a cross-functional setup with competencies in AI research and development to UX/UI design and cost analysis. Throughout the process, continuous experimentation and fine-tuning were critical in aligning the solution with business goals and user needs.
AI interview process design and UX/UI refinement
We created an intuitive user interface that allowed candidates to interact with the AI agent in a simple but effective flow that replicated a real interview experience.
Cost analysis and API usage optimization
A thorough cost-benefit analysis was conducted to keep API usage within budget, ensuring the AI queries were resource-efficient and scalable for production.
Speech-to-text technology integration
We tested and integrated speech-to-text functionality to enhance the interactive experience. This lets users communicate with the AI naturally through voice inputs.
Prompt engineering for quality feedback
By refining prompts on the backend, before they were sent for processing in the model, we could optimize the AI’s ability to generate interview questions and deliver detailed, personalized feedback.
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Solution
We launched the internal proof of concept (POC) in February 2024 and after extensive testing and refinement, we merged our solution with other Monster backend services and went to production in July 2024. Flexible limits on backend resources helped us optimize API usage while maintaining high-quality interview simulations. The final solution helps candidates prepare for interviews with confidence.
Candidates who apply for a job through Monster’s app can prepare for the interview with the AI model. The AI analyzes the job description and user profile to generate five relevant interview questions, simulating a real-world interview environment. Candidates respond through audio or video, and the model evaluates the responses based on content, speed, and clarity.
The AI offers personalized feedback on the candidate’s performance, identifying areas for improvement, such as pacing, missing key points, and more. This feedback enables candidates to better prepare for real interviews, improving their overall chances of success.
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Team Composition
A cross-functional team of developers and designers worked on the AI feature and integrated in it into the main Monster Job Search application.
iOS Developers
Android Developers
UX/UI Designer
Project Manager
QA Engineers
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