Streamline Recruitment Processes and Improve Candidate Matching with ChatGPT
- 6 minutes read - 1073 words
Table of Contents
Recruiting can be a time-consuming process, particularly when reviewing CVs. However, natural languages processing technology advancements, such as ChatGPT and similar large language models (LLMs), can increase productivity for recruiters.
This how-to will outline the strengths of ChatGPT for recruiting and explore some use cases.
Strengths of ChatGPT for Recruiting
ChatGPT is an LLM trained on billions of texts, including CVs. As a result, it has several strengths for recruiting:
- It can translate texts into a broad range of languages, making reviewing CVs from candidates who speak different languages quick.
- ChatGPT can create various outputs, such as summaries and highlights, which can be helpful when reviewing many CVs.
- It can transform any text, including CVs, into a structured format that is easy to analyze.
- Its NLP tasks can be used to analyze texts, making it possible to extract key information from CVs quickly and accurately.
Benefits for the Recruiter using ChatGPT or GPT-4
As a recruiter, ChatGPT can support you in several ways, including:
- Streamlining the CV review process (for example, translations)
- Extracting key skills, technologies, experiences, and other essential information from CVs quickly and accurately
- Reducing the time and resources required for manual CV analysis
- Comparing CVs against job requirements
CV-related Use Cases
Analyzing and extracting information from a CV:
ChatGPT can help recruiters to extract key information from CVs quickly and accurately, such as:
- Skills
- Technologies
- Match with requirements
As a sample CV you can use the synthetic CV here . To use it copy the CV text into the input box of ChatGPT (GPT-4, 3.5 will not work without issues). It will stay in the chat session context.
Prompt:
put this “copied_CV_text” into your context; do not provide detailed output, just confirm
Extract Skills
Prompt:
Extract all skills from CV
The result will look like this:
Here are all the skills extracted from the Synthetic CV:
Java/J2EE
Oracle Database
Spring Framework
Hibernate
HTML/CSS/JavaScript
Agile Methodologies
Extract all skills, and show level of experience
Prompt:
Extract all skills from CV, and years of experience; only the result no comment
Asking GPT-n only the result no comment prevents it from return additional text.
Result will look like:
Java/J2EE - 25 years
…
It might be necessary to repeat the prompt, sometimes the analysis is not complete.
Look for inconsistencies
Prompt:
List all inconsistencies
Result will look like:
The CV claims 25 years of experience in Java backend development, but the individual project durations sum to 27 years and 6 months.
In the “Digital Asset Management System” project, the duration is from 2021/01 to 2022/06, which is only 1 year and 6 months, but it is claimed that the candidate has 1 year of experience in MongoDB. This may be an inconsistency if the candidate only gained MongoDB experience during this project.
The “Workflow Management System” project duration is mentioned as 2023/01 - 2024/06, which is in the future from the current date (2023-04-25). This is an inconsistency in the timeline of the CV.
It might be necessary to repeat the analysis multiple times.
Match with requirements
Prompt:
Assume these requirements: Java min 5 years, HTML min 2 years, Oracle min 3 years, match this with the CV
Result:
Based on the provided synthetic CV, the candidate meets and exceeds the requirements you’ve mentioned:
Java: The candidate has 25 years of experience as a Java backend developer, working on various projects using Java, J2EE, Spring Framework, and Hibernate.
HTML: The candidate has skills in frontend development, including proficiency in HTML, CSS, and JavaScript. Although the CV does not explicitly mention the number of years of experience in HTML, the candidate has been involved in several projects where frontend development was part of their role. Considering their overall experience of 25 years, they likely have at least 2 years of experience in HTML.
Oracle: The candidate has strong expertise in Oracle Database and has worked on multiple projects utilizing Oracle Database technologies, including Oracle 11g and 12c. Their experience with Oracle spans across their 25-year career, easily meeting the 3-year minimum requirement.
In conclusion, the candidate in the synthetic CV appears to be a suitable match for the given requirements.
Translate a CV while keeping structure and format
ChatGPT can translate CVs into various languages while maintaining the original design and layout.
By allowing recruiters to quickly and easily review CVs in different languages without translating each one manually, such tools can help streamline the recruitment process.
This can save a lot of time and effort, especially in multinational organizations that receive a lot of job applications from people who speak different languages.
Prompt:
translate into German: ”cv_text"
Depending on the length of the CV, ChatGPT will not be able to return the result in one go. If ChatGPT has stopped, just enter continue or copy and paste the last 3-5 lines into the input box.
Note: for preserving the formatting do not copy and paste pure text into the input box, instead use markdown formatted text.
It is recommended to use the API for lengthy text output, this is more reliable than using ChatGPT user interface.
As a translated sample CV can be found here .
Post Processing the Output
ChatGPT is not supporting Word or other document formats directly.
Convert CV into another format
Depending on the requirements of the job application, ChatGPT can convert a CV into various formats such as Text, Markdown, XML, or HTML.
Converting into Word and other formats
If you want to convert the ChatGPT text output to Word, paste it into a word processor or use Pandoc and a Python script to convert your CV automatically.
Pandoc is a robust command-line tool for converting documents between formats such as Markdown, HTML, and XML, as well as output formats such as Word and PDF.
Pandoc allows you to transform documents while maintaining their original structure and content quickly.
Conclusions
ChatGPT has several strengths for recruiting, including its ability to analyze texts and extract essential information quickly and accurately.
As a recruiter, using ChatGPT can streamline the CV review process, reduce the time and resources required for manual CV analysis, and improve the quality of candidate matching.