Main image of article How to Build a Digital Twin to Power Your Job Search

Summary

Today’s job seekers are swamped with applications and interviews. In a bid to save time and aggravation, many are turning to large language models (LLMs) such as OpenAI’s ChatGPT, Claude, or Google Gemini to craft AI-powered replicas of themselves: “digital twins” to help supercharge their job search.

These digital twins can help in crafting tailored resumes, simulating interviews, and scouting out ideal job roles. Sounds good, right? But doing it effectively takes planning, the right inputs, and a uniquely human touch.

With that in mind, we’ve devised a step-by-step guide to building your own digital twin, with expert input on how to get the most out of this emerging technology… and what to avoid.

Before you start feeding documents to an LLM, get a clear idea of what you want your digital twin to do. Are you optimizing for job discovery? Application writing? Interview rehearsal?

Ideally, a good twin can do all of that.

“The end goal is you want it to be able to work on your behalf,” says Cristopher Kuehl, vice president of artificial intelligence and data science at Akkodis. “It must be able to reflect your voice, your tonality, otherwise it will be just a generic representation of an LLM.”

Chris Giordano, director of learning design and development at General Assembly, adds that many job seekers are using digital twins to train through some interview questions, or even respond to interview questions for you—but all those capabilities start with teaching the twin who you are. “Creating a digital twin means providing the AI enough information about you that you feel comfortable with what the output is,” he says.

This typically includes uploading a current version of your resume and, if available, sharing links to your online portfolio or GitHub repositories. “All these sources of information are important for the technology to know about you,” Giordano adds.

Because your digital twin is only as good as the information you feed it, that includes your latest resume at a minimum. Beyond that, it’s a good idea to upload links to your portfolio, GitHub, LinkedIn, or even past cover letters.

“Step one is to make sure that the information that you’re providing to the software is as accurate of your experiences as possible,” Giordano says. “I’d use it to help you with your resume first before having it generate content.”

Kuehl emphasizes pulling together five or six ways you’ve written a response to an email, or using transcription tools like Otter to record how you answer common interview questions.

One of the key things, especially with the newer models that are coming out, is offering feedback on how to be more effective with prompting. “If you don’t like the answers, tell the tool how to get more effective at the prompting,” Kuehl adds. “Make sure you say, ‘That does not sound right; this sounds right.’”

Training your digital twin isn’t just about dumping documents—it’s also about context. Giordano recommends building a custom GPT if you’re using a paid LLM version.

“That way, you’re only providing information about yourself one time, and then it’s able to utilize what you’ve trained it on for job searches, interview prep, and career recommendations,” he says.

Meanwhile, Kuehl advises writing out your job search goals in story form. “Back in high school or college, you always had the exercise where you explained where you want to be in five years,” he says.

He says feeding the LLM that narrative helps the AI learn your aspirations and tailor results accordingly.

A critical component of an effective digital twin is tone and voice. How it writes and speaks for you is critical.

“I would recommend providing a piece of writing that you have used in a professional capacity,” Giordano explains. “Certainly, scrub it of any sensitive information, but an email you wrote applying for a previous position would be a good way for AI to recreate your writing style.”

Both experts stress the need to edit and guide your twin to avoid generic, buzzword-heavy language. “LLMs are really known for using extreme buzzwords,” Kuehl cautions. “This includes terms like ‘cutting edge, breakaway, synergy’-- if you read it out loud and it doesn’t feel good to you, guess what? It doesn’t feel good for anybody.”

Once trained, your digital twin can support nearly every step of the job application process—if you feed it the right inputs. That starts with the job itself.

“If you’re going to be preparing for an interview, you would certainly want to feed the large language model the job description of the role that you are interested in,” Giordano says.

He emphasizes the need to include not just your resume, but also the job post, the company name, and the industry, so the model can tailor its responses to the specific opportunity.

The twin can then simulate interviews, draft responses to application questions, and even help you identify the right roles. “Once it knows enough about you, you can direct it towards asking questions like, ‘Based on my past experiences and a few of my interests, what jobs would you recommend that I would be a good candidate for?’” Giordano says.

Kuehl adds you can go further by writing out your career goals in narrative form to help the AI match you to the right companies and cultures: “Write it as a story format, with the idea of imparting if you’re okay being part of a big tanker ship company, or if you want to be somebody that’s a downhill skier that’s moving super-fast.”

This kind of structured context helps the AI better align its suggestions with your values and long-term plans.

Even with a well-trained twin, you should never treat AI-generated output as ready to submit.

“It should be seen as a first draft and not a final draft,” Giordano says. He recommends a thorough review process that focuses on key areas like tone, accuracy, and confidentiality.

Kuehl also warned against blindly trusting generic responses or over polished language. “Read the text out loud. That’s the biggest thing that I feel will catch you from sending something too generic,” he says. “If it doesn’t feel good for you, it doesn’t feel good for anybody.”

He added that job seekers should be especially vigilant about vague claims, buzzwords, or unnatural phrasing: “Don’t let your GPT go too crazy, or it will over embellish.”

Ultimately, the goal is to collaborate with the AI—not to let it replace you. You want it to be able to act on your behalf.

Giordano echoes this sentiment, emphasizing the importance of retaining your own voice and values in the final product. “Just doing a copy and paste into an application field box is risky,” he says. “You want to keep a human—in this case, you—in the loop.”

Selecting the right LLM is a critical step, and while many job seekers default to ChatGPT, there are other tools worth considering.

“There’s no right or wrong answer on what LLM to start with,” Kuehl says. ““The key thing is to look for the flagship models.”

He recommends beginning with a simple search to find the most current and advanced models available, whether from OpenAI, Anthropic, Google, or Meta. From there, consider each model’s strengths.

Beyond model performance, access and privacy are crucial factors. Giordano emphasizes the importance of using paid versions of LLMs to protect your data, and by using a paid version that supports memory or custom GPTs, you can build a more personalized experience.

Both experts say AI success depends on iteration, not perfection on the first try. “You’re not going to get perfect right off the bat, so you do need to take the time to adjust the prompt,” Kuehl says.

Advanced users may even consider using multiple models for different parts of the job search. “I personally use four unique ones because I need to have a social aspect, I need to have a business aspect, I need to have a personal aspect, and then I need to have an educational aspect,” Kuehl says.

Whether you choose one model or several, the priority should be finding a setup that aligns with your goals, offers strong data control, and supports repeatable, realistic interactions—that’s digital twinning done right.

Want a quick step-by-step on building a digital twin for your job search? Here you go:

  • Clarify Your Objectives: Before you begin, clearly define what you want your digital twin to achieve. Will it focus on job discovery, application writing, interview preparation, or a combination of these?
  • Aim for Versatility: Ideally, develop a digital twin capable of assisting with all aspects of your job search.
  • Gather Comprehensive Information: Your digital twin's effectiveness hinges on the quality and breadth of the information you provide.
    • Resume is Key: At a minimum, upload your most current and accurate resume. Use the twin to help refine your resume first before generating other content.
    • Expand with Professional Links: Include links to your online portfolio, GitHub repositories, and LinkedIn profile.
    • Leverage Past Communications: Upload past cover letters and even examples of how you've responded to professional emails (ensure sensitive information is scrubbed).
    • Capture Your Spoken Style: Use transcription tools (e.g., Otter) to record yourself answering common interview questions to provide the LLM with examples of your verbal communication style and tonality.
  • Ensure Authenticity: The goal is for the twin to reflect your unique voice and tonality, not a generic LLM output.
  • Provide Ample Personal Data: The AI needs sufficient information about you to produce outputs you're comfortable with.
  • Consider Custom GPTs: If using a paid LLM version that supports it, build a custom GPT. This allows you to provide your information once and have the AI utilize it for various tasks like job searches, interview prep, and career recommendations.
  • Share Your Career Narrative: Write out your job search goals and five-year career aspirations in a story format. This narrative helps the AI understand your ambitions and tailor results accordingly.
    • Specify your preferences for company culture (e.g., large, stable "tanker ship" company vs. fast-moving "downhill skier" startup). This context helps align suggestions with your values.
  • Input Your Writing Style: Provide a piece of professional writing (e.g., an email applying for a previous position, scrubbed of sensitive data) to help the AI recreate your specific writing style.
  • Iterative Feedback is Crucial: Don't expect perfect results immediately. Be prepared to offer feedback to the LLM.
  • Correct and Guide: If you dislike the AI's responses, explicitly tell the tool. For example, say, "That does not sound right; this sounds right," to guide it.
  • Beware of Buzzwords: LLMs tend to use excessive buzzwords (e.g., "cutting edge," "synergy"). Edit these out if they don't sound authentic to you.
  • Read Output Aloud: This is a critical step to catch generic, over-embellished, or unnatural phrasing. If it doesn't sound good to you, it won't sound good to a recruiter.
  • Don't Let it "Go Too Crazy": Be vigilant about over-embellishment or vague claims.
  • Tailor to Specific Roles:
    • For interview preparation, always feed the LLM the specific job description.
    • Include the job post, company name, and industry along with your resume for tailored responses.
  • Simulate Interviews: Use the twin to practice answering interview questions.
  • Draft Application Materials: Let the twin assist in drafting responses to application questions.
  • Discover Suitable Roles: Once sufficiently trained, ask your twin: "Based on my past experiences and a few of my interests, what jobs would you recommend that I would be a good candidate for?"
  • Treat AI Output as a First Draft: Never consider AI-generated content as final and ready to submit without review.
  • Thoroughly Review: Focus on tone, accuracy, and confidentiality.
  • Retain Your Voice: Ensure your personality and values are reflected in the final product. Avoid a simple copy-and-paste from the AI into an application.
  • You Are in the Loop: The goal is collaboration with AI, not replacement by it. You are the final arbiter of what gets submitted.
  • No Single "Right" Answer: The key is to look for flagship models from major providers (OpenAI, Anthropic, Google, Meta).
  • Start with a Search: Find the most current and advanced models available.
  • Consider Paid Versions for Privacy and Features:
    • Paid versions often offer better data protection.
    • They may support memory or custom GPTs for a more personalized and efficient experience (inputting your data once).
  • Iterate with Prompts: Success depends on adjusting your prompts; you won't get perfect results on the first try.
  • Consider Multiple Models (Advanced): Advanced users might use different LLMs for various aspects of the job search (e.g., social, business, personal, educational).
  • Prioritize Alignment and Control: Choose a setup that aligns with your goals, offers strong data control, and supports repeatable, realistic interactions.
  • Accuracy is Paramount: Ensure all information provided to the LLM about your experiences is as accurate as possible.
  • Start with Resume Optimization: Use the digital twin to help with your resume before having it generate other content.
  • Human Touch is Irreplaceable: The digital twin is a tool to augment your efforts, not replace your critical thinking, personal touch, and final judgment.