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Enhance Recruiting with AI-Generated Interview Questions

by Divya Cheerla, Data Engineer, Rackspace Technology

According to a study conducted by the Harvard Business Review, 80% of employee turnover is attributed to hiring mistakes. What if your interview questions could help prevent this turnover?  Traditional interview question preparation can be time-consuming and often leads to generic questions that may not effectively assess a candidate’s suitability for the role.

The solution for many organizations could be found in Rackspace Intelligent Co-worker for the Enterprise (ICE™), an AI-powered web application designed for human resources teams to ease the burden of interview question generation for recruiters and hiring managers. This application, built utilizing Azure OpenAI services, facilitates the generation of personalized interview questions for job candidates, significantly streamlining the hiring process and making it more effective.

Overall functionality is straightforward: The application prompts a user to enter details such as the candidate’s role, company, job description and, optionally, allows the user to upload a resume in PDF format or select a particular category of questions they want to generate.

Once the user clicks the “generate” button, the application processes the input data and leverages AI and natural language processing to create tailored interview questions that are relevant to the candidate’s skills, experience, job description, role and the company. The output is displayed in a text box that can be downloaded as a PDF for further use in the hiring process.

Watch a short demo video for Rackspace ICE ->

This use case for generative AI brings several significant benefits to recruiters and hiring managers. By automating the interview question generation process, the application saves valuable time and effort and makes the hiring process more efficient. This AI-driven approach to hiring helps to ensure that interview questions are personalized to each candidate’s qualifications, enabling HR to conduct more effective interviews and more accurately assess a candidate’s suitability for the job.

The enhanced candidate experience that results from stronger engagement can also showcase the company’s commitment to personalized interactions, which can leave a positive impression on potential hires. The result is a more efficient and accurate hiring process that enables companies to identify the most suitable candidates and make better-informed hiring decisions.

Potential use cases of Rackspace ICE extend far beyond hiring to include:

  • Automated report generation
  • Content summarization
  • Personalized recommendations
  • Automated content creation
  • Customer support
  • Content tagging
  • Content analysis for market research
  • Automated legal document generation
  • Automated coding assistance
  • Content moderation

Rackspace ICE architecture

ICE Architecture


The architecture above shows how the application streamlines the interview question generation process by allowing users to input their details and upload a PDF resume. Leveraging LangChain modules, the application extracts key information from the uploaded files. This data, along with user input such as role, company, job description, and selected question categories, is then sent to Azure Open AI. Azure Open AI utilizes this information to generate tailored interview questions based on the candidate’s skills, experience, job description, role, and company. The output is displayed in a text box and can be conveniently downloaded as a PDF for future reference. Additionally, the files uploaded are stored in Azure Blob Storage, providing a robust archival system for future use.

Advanced features and potential use cases

Beyond the core functionality of interview question generation, the application holds the potential for advanced features that include:

  1. Sentiment analysis: Analyzing candidate resumes and applications to gauge enthusiasm and suitability for the company culture
  2. Integration with candidate tracking system: Streamlining the hiring workflow by integrating the application with existing candidate tracking systems
  3. AI-driven candidate ranking: Prioritizing potential candidates based on their qualifications and compatibility with the role and company

Citation: The Biggest Thing Wrong With Hiring Is the People Doing It | Inc.com

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