A good resume remains one of the main tools in a job search. Yet many candidates spend hours structuring their experience, choosing the right wording, and preparing cover letters. It can be especially hard to describe achievements in a way that looks convincing to an employer.
AI helps speed up this work. Neural networks can structure experience, improve wording, adapt a resume to a specific job posting, and point out weak spots in the text. At the same time, responsibility for facts, numbers, and skills stays with the person.
Step 1. Prepare your data
Before opening a neural network, gather information about your experience. Without concrete data, AI will generate generic phrases that look little different from hundreds of other resumes.
Fill in a table for each job.
FieldExamplePosition and companySales manager, Example LLCPeriodMarch 2021 — June 2023Main tasksFinding clients, negotiations, deal supportResultsIncreased sales volume by 35% in a yearToolsBitrix24, amoCRM, Excel
If you don't remember exact figures, check old reports, your CRM system, or work correspondence.
Instead of "handled sales," formulate a specific result right away: "brought in 50 new corporate clients in a year" or "increased average order value by 30%." Such details make a resume stronger and help AI prepare a higher-quality document.
Step 2. Resume draft
Once your data is ready, move on to generating the first version of your resume.
Sample prompt:
Write a professional resume for a sales manager position.
My experience:
[describe your experience]
My achievements:
[describe your achievements]
Skills:
[list of skills]
Make the resume in a modern format with an emphasis on measurable results.
After getting the result, continue the dialogue:
- Which phrases sound generic? Replace them.
- Trim the skills section to the 8 most important items.
- Make the achievements more specific.
- Find weak spots in the resume.
Step 3. Adapt to the job posting
Many companies use an ATS (Applicant Tracking System) — automated candidate screening systems. They analyze resumes before the document even reaches a recruiter. If a resume lacks the keywords from the job posting, the candidate may not pass the initial screening.
Sample prompt:
Here is my resume:
[resume text]
Here is the job description:
[job posting text]
Analyze the job requirements and adapt the resume to them. Don't invent experience that isn't there.
For example, if the posting requires B2B sales experience, work with large clients, and meeting quarterly targets, the neural network will help highlight the relevant experience in the resume and strengthen the description of achievements.
Only make changes that match your real experience. Save each adapted version as a separate file named after the company.
Step 4. Cover letter
A quality cover letter can make an application stand out among dozens of others. A good letter shows motivation and explains why exactly you are a fit for this position.
Standard structure:
- Brief introduction.
- Reason for interest in the company.
- One relevant achievement.
- Value for the employer.
- Call to further communication.
Sample prompt:
Write a cover letter for a sales manager position.
My experience:
[describe your experience]
My achievements:
[describe your achievements]
Keep the letter under 1500 characters.
Avoid generic phrases and vague reasoning.
It's better to write the reason for your interest in the company yourself. This is one of the few places where personal insight works better than any neural network. One specific reason helps you stand out among generic applications.
Step 5. Check the wording
Even a good result is worth an extra check.
Use a separate prompt:
Analyze my resume as an HR specialist.
Find:
- generic phrases;
- weak wording;
- repetitions;
- points without a specific result.
Suggest improved versions.
This prompt helps get rid of typical phrases like "responsible," "communicative," "fast learner," "stress-resistant." Recruiters aren't interested in personal traits by themselves, but in work results and achievements.
Step 6. Final checklist
Do this check yourself. AI doesn't know what's true in the text and what's exaggerated.
Facts
Check that:
- employment dates are correct;
- numbers match reality;
- company and position names are spelled correctly;
- contact details are up to date.
Content
Make sure that:
- there are no skills you can't back up in an interview;
- there are no generic phrases without specifics;
- every point contains a work result.
Match with the job posting
Before sending, check that:
- keywords from the job posting appear in the resume;
- the most relevant experience is placed above the rest;
- the cover letter is written for the specific company.
Which tools to use
ChatGPT
A universal option for building resumes, analyzing job postings, and preparing cover letters. Does well at improving wording and adapting text.
Claude
Convenient for working with long documents and detailed analysis of job postings. Often used for editing a finished resume.
Gemini
Helps research a company before sending an application and find ideas for strengthening a professional profile.
Common mistakes
Copying the result without checking
AI can add invented achievements, inaccurate dates, or overstate competencies. The first draft almost always needs edits.
Sending the same resume to every job
Adapting to each position takes a few minutes, but noticeably increases the chance of getting invited to an interview.
Leaving in achievements that aren't yours
Everything written in a resume will have to be backed up in an interview. If AI added a skill or result that wasn't there, it's better to remove it.
Ignoring the cover letter
A personalized letter shows the candidate's genuine interest and helps them stand out among other applicants.
Takeaways
AI makes it possible to noticeably cut the time spent preparing a resume and cover letter. Instead of spending hours choosing wording and restructuring the document for every job posting, you can use a neural network to create a draft, analyze the text, and adapt it to the employer's requirements.
That said, AI shouldn't be treated as a replacement for your own experience and common sense. A neural network helps format information, but it doesn't know what results you actually achieved or which skills you can back up in an interview. That's why any numbers, achievements, and wording need to be checked before sending an application.
The most effective approach looks like this: first gather the facts about your experience, then use AI to prepare the resume and cover letter, and after that manually check the result and adapt it to a specific job posting. This process takes less time and helps produce a stronger document.
Where to follow AI tool developments
If you want to compare answers from different neural networks and choose the best option for working on your resume, it's convenient to use services with access to several models in one interface. For example, through GPTunneL you can work with ChatGPT, Claude, Gemini, and other popular AI tools without switching between different platforms.