Job searching involves more than just polishing your CV: selecting job offers, presenting experience, filling out forms, networking, and preparing for interviews. This guide provides nine prompts to help organize these stages. Their starting point is your facts, not a promise to bypass the recruitment system.
Last verified: September 6, 2026. Informational and educational material. Recruitment systems and AI tools change every few months; rules regarding personal data and discrimination in recruitment vary between countries and states. This guide is not legal or immigration advice.
Update on CV and ATS section, October 7, 2026: We clarified file reading, matching scores, prompts, and federal CV limits. A new practical guide to preparing a CV with AI includes eight prompts, a full example, and downloadable materials.
In Brief
- ATS has various functions. Document reading, searching, matching, and automatic rejection rules are separate mechanisms. Their operation depends on the system and company settings. Greenhouse describes reading issues and rejection rules based on form questions. A reading error does not automatically mean rejection in this system.
- Preparing for ATS is not about tricks: use a readable format, show requirements from the offer backed by facts, and fill out the form truthfully. A referral may help reach people, but it does not guarantee bypassing the process.
- Everyone uses the same AI, so the advantage lies in the data that AI does not have: your numbers, projects, names, artifacts. Prompt 0 builds a fact file from them; each subsequent prompt uses it and is prohibited from making things up.
- Avoid generalities. Words like “spearheaded,” “leveraged,” or “passionate” do not prove AI authorship. The problem is the lack of specific information about your actions and results.
- You have the right to know that an algorithm is evaluating you. In New York, an employer using automated tools for hiring decisions must have a bias audit and notify the candidate (NYC, Local Law 144); the federal EEOC is conducting an initiative on AI in employment (EEOC).
- Check various job search channels. Prompt 6 helps prepare contacts and messages, and prompt 8 compares application and interview results. The effectiveness of channels depends on the role and situation.
How ATS Really Works
The Applicant Tracking System (ATS) helps companies collect applications and manage the process. It can read documents, provide searching and filtering, ask questions, and support matching evaluation. Not every employer uses all features. In Greenhouse's Talent Matching, matching depends on criteria set by the recruitment team, and the function itself does not reject candidates.
A report from Harvard Business School and Accenture titled “Hidden Workers: Untapped Talent” (report page) described the consequences: filters set on employment gaps, lack of a degree, or missing exact words from the job posting reject people who can perform the job. The authors estimate this group in the USA at 27 million people. For immigrants, this is particularly important: a gap for relocation, a degree from another country, and different job titles are exactly those filters.
Do not treat popular numbers about CV rejection as a diagnosis of your application. Without knowledge of the system and process, you will not determine why the company did not respond. Focus on a readable document, evidence of matching, and truthful answers in the form.
The parser extracts text and assigns it to fields. Columns, graphics, and unusual layouts can hinder reading. Copying text to a notepad helps detect some issues, but it is not an accurate simulation of the parser. After uploading, check the form fields.
The form may ask about licenses, work eligibility, availability, or education. If the company sets a screening rule, a specific answer may lead to automatic rejection. Answer truthfully.
The recruiter may use searching and filters, as well as read attached documents. A referral from an employee is an additional contact channel; its handling depends on the company and does not guarantee a separate queue.
Why CVs from ChatGPT Look the Same and How to Be Better
A language model asked to “improve my CV” does the same for everyone: it adds the same verbs, smooths sentences, invents numbers where there were none, and produces a cover letter that the recruiter has already seen forty times today. The advantage does not lie in a better writing prompt. It lies in four things that the model does not have until you provide them:
- Data. Numbers, system names, team sizes, amounts, deadlines, artifacts (repository, portfolio, publication, certification). Prompt 0 extracts them from you through an interview before anything is written.
- Matching. One CV for one job posting, with exact phrases from that posting, but only where you have evidence for them. Prompt 2 reads the posting, prompt 3 matches.
- People. A person in the company who will pass on the application, and an informational interview before applying. Prompt 6.
- Loop. Each week you look at which channels yield responses and change your method. Prompt 8.
The right role of AI – researcher, job ad analyst, interview coach, and editor working on your facts. Not the author of your story.
Before You Paste the First Prompt
One Place for All Job Searching
Job searching takes weeks, and the prompts in this guide use the same fact file. Set up one place: in Claude, it’s a Project where you attach the fact file and enter fixed instructions (Anthropic documentation); in ChatGPT, it’s a Project with files and your own instructions (OpenAI help on projects, on custom instructions). In the fixed instructions, write one sentence that saves you from the biggest mistake: “Never add achievements, numbers, names, or skills that are not in the fact file; if something is missing, ask.” You enable web searching in Claude from the “+” menu (instructions), in ChatGPT it is in search mode (OpenAI help); Gemini has a Deep Research mode (Google description) useful for researching companies.
One Calibration Question
Before we start: provide today’s date. I am looking for a job as [POSITION] in [CITY / state / remote]. List five job titles under which employers advertise this role, with links to three current job postings, and a link to the job description in the O*NET database. If you cannot find something, write “I did not find it.”
If the links are dead, the postings are old, or the job titles are synonyms from memory, the model is not searching. Reply: “Search the web and provide current postings with dates.” The O*NET database contains official job descriptions, skills, and alternative job titles; it is the best source of words that recruiters are looking for.
Where the Model Should Search
| What You Need | USA | Poland |
|---|---|---|
| Job titles, skills, keywords | O*NET; MySkillsMyFuture (Department of Labor) for translating skills between jobs | Postings from major portals; job classification on praca.gov.pl |
| Salaries and job outlook | Occupational Outlook Handbook (Bureau of Labor Statistics) | GUS, salary reports from portals (secondary data) |
| CV for federal jobs | USAJOBS: what to include in a CV (different rules than in the private sector) | Civil service recruitment: announcements from offices |
| Recognition of Polish diploma | Evaluation by an organization from the list of NACES, e.g., WES | Validation of foreign diploma at a university |
| Work eligibility and documents | USCIS: documents for Form I-9 | guide on work legalization in Poland |
| AI in recruitment and your rights | NYC Local Law 144, EEOC, EEOC for candidates | UODO (personal data in recruitment) |
| Signal “I am looking for a job” | LinkedIn: Open to Work | The same |
| Recruitment scams | FTC on fake job offers | Our guide on fake job offers |
Brackets in Prompts
Brackets stand for placeholders: [POSITION], [CITY], [COMPANY], [POSTING]. Replace them once. Each bracket has a header indicating where to paste it, and a “Copy prompt” button. Do not enter your social security number, date of birth, document numbers, or address in the fact file; your name, surname, city, and work history are sufficient.
Scheme: Nine Steps in One Project
- Fact File
The model interviews you about your entire work history and builds an inventory of achievements with numbers, names, and evidence. Without this file, everything else is made up.
- Goal and Market Map
What does the job you are looking for look like, under what names is it advertised, where, for how much, and what is required. A list of companies and criteria.
- Job Posting Analysis
Hard and soft requirements, exact words, screening questions, hidden signals, matching evaluation without flattery, whether it is even a real offer.
- CV for the Posting
Matching from the fact file in a format that the parser reads. A list of changes so you know what has changed. Parsing test.
- Form and Screening Questions
Work eligibility, experience, salary, relocation: how to answer truthfully and understand the question.
- Message, Letter, and Profile
A short text that no one else will write, a LinkedIn profile consistent with the words from the postings.
- People
A map of people in the company, a request for a referral, an informational interview. An additional contact channel.
- Interview
Questions arising from the posting, answers from the fact file, a mock interview with critique, company research with sources.
- Weekly Loop
An application table, what works, what to change. You read the offer according to a separate guide.
Why in this order. Facts before writing, because the model makes things up without facts. Goal before postings, because without a goal you apply for everything. Job posting analysis before CV, because the CV matches specifics. People after documents, because a referral request requires a ready CV. The loop at the end, because it measures the whole.
Prompt 0 – Fact File
The most important step and the only one where you speak, and the model asks. Goal: a document with every achievement, project, tool, number, and name that can be extracted from you. Recruiters look for specifics; models without specifics produce adjectives. Do this once, come back after a week and add what you remembered.
You are my career editor. Your task: to build a fact file about my work history that we will use for every CV, message, and interview. You are not writing a CV yet.
RULES
– You ask, I answer. Do not add anything that I did not say. If the answer is general (“I improved processes”), ask for numbers, scope, tools, time, and outcome until it is specific or I say “I don’t know.”
– Conduct the interview one position at a time, starting with the most recent. After each position, show me the collected facts and ask if anything is missing.
– Record job titles, tools, and industry terms in Polish and English so I have both variants.
Start with questions about: current or last position (title, company, industry, size, dates, team, who I reported to); what I was responsible for in numbers (budget, clients, revenue, users, transactions, submissions, projects); three things I changed and what improved because of it; tools and systems with proficiency level; certificates and training with dates; artifacts I can show (portfolio, repository, publications, presentations); languages with proficiency level; situations I would talk about in an interview (problem, action, result).
After going through all positions, return the FACT FILE in the format: sections by position, in each points “fact: number: evidence,” a separate list of skills with levels, a separate list of stories for interviews. At the end, a list of gaps: things you asked about that I could not provide, so I can check in old documents.
Check in the response: whether the file contains anything you did not say. Models “fill in” facts in good faith; read the entire file and cross out any number you are not sure about. Save the file outside the chat and attach it to the project; it will be the source for all subsequent prompts.
Prompt 1 – Goal and Market Map
“Dream job” is a set of criteria, not a title. This prompt turns it into a list: what positions, under what names, in what companies, for how much, with what boundary conditions, and what you lack to be a candidate, not just a willing applicant. You check salaries and outlook in the Occupational Outlook Handbook, job titles and skills in O*NET.
Same conversation, the fact file is in the project. Help me define my goal and market map.
MY GOAL DESCRIPTION
– What I want to do daily: [description]. What I don’t want: [description].
– Location: [CITY / remote / relocation yes or no]. Mode: [office / hybrid / remote].
– Salary: minimum [AMOUNT], target [AMOUNT]. Benefits I care about: [e.g., visa sponsorship, family insurance].
– Boundary conditions: [e.g., no shift work, no travel over 20 percent].
– Horizon: I want to have an offer within [e.g., 3 months].
RETURN
1. A list of job titles under which such work is advertised, with the code and description from O*NET and five alternative titles used by employers. For each title, a link to a current posting with the date.
2. Salaries: median and range for this occupation in my region from a government source with the date, next to the ranges from current postings. Does my target fit within the market.
3. Requirements that recur in postings: the ten most common skills and tools with the number of occurrences in [e.g., 20] postings you will review. Separately: formal requirements (degree, license, certification, work eligibility).
4. My matching: a table of requirements from point 3 next to evidence from the fact file or “none.” Honestly. For each absence: can it be closed within a month (course, certification, project), a quarter, or not at all.
5. A list of twenty companies that hire for these positions in my region, categorized: large with developed ATS, medium, small where the application goes to a person. For each: a link to the careers page.
6. Three strategies with an assessment that fits my horizon: mass applying through portals, targeted applying with a referral, entering through temporary or contract work. No recommendation for one; state how much time each requires weekly.
Check in the response: whether the O*NET code and salary link exist and lead to the correct occupation; the model is eager to provide codes from memory. The matching table in point 4 must contain “none” where you do not have it; if everything is green, the model flatters. If your degree is from Poland, check in postings whether employers require evaluation by an organization from the list of NACES.
Prompt 2 – Job Posting Analysis
A job posting has three layers: what they really require (usually three to five things), what they pasted from a template, and what they did not write (level, visa, on-site, why the position is open). This prompt breaks them down, extracts exact phrases that the recruiter will use in the search engine, and evaluates the match without flattery. It also checks whether the offer is real; scam patterns are described by the FTC.
Same conversation. Below, I paste the job posting for [COMPANY], [POSITION]. Read it like a recruiter who will search the candidate database.
RETURN
1. Hard requirements (without them I am out) and soft (preferred), each with a quote from the posting. Separately, formal requirements: work eligibility, degree, license, location, hours.
2. Keywords in the exact wording from the posting: names of tools, methods, certifications, titles. For each: do I have evidence for it in the fact file (yes, with what fact; partially; no). Do not change the wording, even if I have a synonym in the fact file; note the difference.
3. Screening questions I expect in the form based on the content of the posting (years of experience, work eligibility, visa sponsorship, relocation, salary, availability). For each: what answer ends the process.
4. Hidden signals: the level of the position based on the scope of duties and ranges, whether the position is new or replacing someone, whether the team is being built, whether the posting is a copy of a template (the same sentences in other postings from this company), how long it has been posted, whether ranges are provided, and if the state requires it and they are not present, note that.
5. Red flags for scams: contact through a messenger, request for bank details or payment, email address from a domain other than the company’s, salary disproportionate to requirements, absence of the company in registries. Check if the posting is also on the company’s careers page.
6. Matching score from 1 to 10 with justification: three strongest matches with evidence and three gaps. Write directly whether to apply, apply with a referral, or skip, and why.
7. Five questions I would ask the recruiter before applying if I had access to them.
POSTING:
[POSTING]
Check in the response: keywords in point 2 must be in the wording from the posting; the parser and search engine do not know synonyms as well as the model. A score in point 6 below 6 with hard gaps is a signal not to waste time; a model that gives all postings an 8 needs to be calibrated with one sentence: “Evaluate strictly, like a recruiter with a hundred candidates.”
Prompt 3 – CV for the Posting
One CV for one posting, from the fact file, with exact phrases from the analysis, but only where you have evidence. Simple format: one column, standard section headers, plain font, no tables, icons, graphics, or text in the header; PDF with text, not a scan. In the USA, no photo, date of birth, marital status, or nationality; these are data that the employer should not consider (EEOC). For federal jobs, there is a limit of two pages and additional data requirements, including hours worked weekly (OPM).
Same conversation. Based on the fact file and analysis from prompt 2, prepare a CV for this posting.
RULES
– Each sentence in the CV has a source in the fact file. Do not add achievements, numbers, tools, or titles that are not there. If something is missing for a requirement from the posting, leave a gap and list it separately.
– Use keywords from prompt 2 in the exact wording, in the context of the fact that confirms them. Do not insert a list of words without context and nothing hidden.
– Prohibited: spearheaded, leveraged, passionate, results-driven, synergy, dynamic, go-getter, team player, detail-oriented, proven track record, and any sentence that someone else could write for this position. Instead of adjectives: numbers, names, outcomes.
– Format: one column; sections in this order and with these names: Summary, Experience, Skills, Education, Certifications; for each position, title, company, city, month and year from and to; bullet points starting with a verb in the past tense; no tables, icons, graphics, header, columns, or text boxes. Length: [1 page / 2 pages].
– The job title in the CV header should match the title from the posting if my actual title means the same; if not, leave mine and add the equivalent in parentheses.
RETURN
1. CV ready to paste.
2. LIST OF CHANGES: what and why changed compared to my previous CV, point by point, with reference to the requirement from the posting.
3. GAPS: requirements from the posting that have no evidence in the fact file, and how I can close them or address them honestly in a message to the recruiter.
4. READABILITY CHECK: a plain text version of the CV, without pretending to read a specific ATS; check if dates, titles, and companies are recognizable in each line.
5. Three questions for me if any fact needs clarification before I send it.
Check in the response:
- Read the CV sentence by sentence with the fact file next to it. Any number not in the file gets removed. This is the moment when people send CVs with achievements they did not have and get caught in the interview.
- Do a parsing test yourself: copy the finished PDF to a plain notepad. If the order of sections falls apart or dates are detached from positions, the format needs improvement.
- Search the CV for words from the prohibited list and their Polish equivalents (“responsible for,” “dynamic,” “results-oriented”). The model breaks its own prohibition more often than it seems.
- Save a version of the CV with the company name and date; during the interview, you need to know exactly what you sent.
Prompt 4 – Form and Screening Questions
The ATS form asks questions where a “no” answer closes the process, and questions where “too much” or “too little” harms. You answer truthfully; the goal is to understand what they are asking and not to drop out due to misunderstanding. The most common misunderstanding among the Polish community: the question about work eligibility and about visa sponsorship are two different questions; documents confirming eligibility are described by USCIS.
Same conversation. Prepare me for the application form in ATS for this posting. My status: [citizen / permanent resident / visa with work rights, which and until when / I need sponsorship]. My salary expectations: [AMOUNT or range].
RETURN
1. A list of screening questions you expect in this form, with an explanation of what they are really asking, and with a truthful answer for my situation. Separately: the question about work eligibility and the question about visa sponsorship, now and in the future; explain the difference and what to answer in my case.
2. Years of experience: how to count for each requirement from the posting based on the fact file, broken down by positions, so I can provide a number I can defend.
3. Salary: what to enter if the field is mandatory, and how it relates to the ranges from prompt 1; if the state requires ranges in the posting, and they are not present, what does it mean.
4. Fields not to guess: if the form asks for something not in the fact file (license number, graduation date), list it so I can check in documents.
5. Questions about sensitive data (origin, gender, disability, veteran status): explain why the form collects them, that they are voluntary, and that the answer should not affect the decision; source.
6. Attachments: what to send besides the CV, in what format, how to name files, and whether a cover letter is required or optional.
7. If the employer uses AI tools to evaluate candidates: whether in this state or city they are required to notify me and what I can do; source.
Check in the response: answers in point 1 must be true for your situation; the model does not know your status better than you, and an untrue answer about work eligibility has consequences far beyond this application. Point 7: in New York, there is an audit and notification requirement (Local Law 144); in other places, the model should say “I did not find a regulation,” not guess.
Prompt 5 – Message, Letter, and Profile
A cover letter from AI is today recognizable by the first sentence. If it is optional, a better approach is a short text to the recruiter: three sentences that no one else could write because they relate to a specific fact from your history and a specific problem of that company. The LinkedIn profile should contain the same words that recruiters are looking for, as they search there just like in ATS; the “I am looking for a job” signal is set in the profile (LinkedIn: Open to Work).
Same conversation. Prepare texts for this posting and the profile.
RULES
– Uniqueness test: each sentence must contain something from the fact file or from research on this company that no one else has in their fact file. General sentences (“I am motivated,” “Your company is a leader”) should be discarded.
– Prohibited words from prompt 3. No exclamation marks. No repetition of the CV.
– Company research only from sources with links and dates: company website, press releases, postings, public results. Do not invent facts about the company.
RETURN
1. A message to the recruiter or manager (up to 90 words): one specific fact about the company with a source, one fact from my history that addresses the main requirement from the posting, one sentence about what I want. Version in English and Polish.
2. A cover letter (up to 200 words), only if the posting requires it: the same structure, plus one sentence about a gap from prompt 3 and how I am closing it.
3. LinkedIn headline (up to 220 characters) and “About” section (up to 1200 characters) with job titles and keywords from prompt 1, in the context of facts; no list of words.
4. The first three lines of experience in the profile, rewritten to include words from the postings I am targeting, while maintaining truthfulness.
5. A list of ten skills for the Skills section in the profile, in the wording from the postings, only those for which I have evidence.
6. A follow-up message after applying (up to 60 words) to be sent after 5 to 7 days of silence, and a version after the interview with thanks, referencing one specific topic from the conversation (leave space to fill in).
Check in the response: click on the source about the company from point 1; models invent “recent implementations” and “awards.” Read the message aloud: if it sounds like a message you would delete, shorten it by half. Do not paste anything into LinkedIn that is not in the CV; recruiters compare both.
Prompt 6 – People
A referral from an employee can help pass the application to the right person, but it does not guarantee an interview or bypassing ATS. An informational interview helps understand the role. This prompt organizes the map of people and message proposals. Prepare the contact list yourself and check current positions; do not assume the model has access to LinkedIn.
Same conversation. I want to reach out to people at [COMPANY] and in companies from the list in prompt 1 as an additional channel besides applying. Below, I paste who I found: [list: name, position, how we are connected: former colleague, same university, Polish community, mutual acquaintance, no connection].
RETURN
1. Order of contact: from people with a real connection to strangers; for each: why this person, what to ask them (referral, informational interview, information about the team), what not to ask.
2. Three messages (up to 70 words each), in English and Polish: to a former colleague, to a person from the same university or community, to a stranger in the position I am applying for. Each with one specific fact about me from the fact file and one specific request with a deadline. No attaching the CV in the first message.
3. Informational interview: ten questions that give me words and information useful in the CV and interview (what a day really looks like, what the team is missing, what the recruitment process looks like, who decides), without asking about a job directly.
4. Request for a referral: a message (up to 80 words) to someone I have already spoken with, with a link to the posting, one sentence about why I fit, and a ready paragraph they can paste into the referral system so they don’t have to write anything.
5. Weekly plan: how many messages, when to follow up (after 5 to 7 days, once), when to give up.
6. Places where people in this industry in [CITY] meet in person or online, with sources and dates; including Polish and professional organizations.
Check in the response: messages must be shorter than the model wants; if they exceed the limit, ask to shorten. Do not send the same message to ten people in one company; people talk to each other. Check meetings from point 6 in the source; the model invents group names.
Prompt 7 – Interview
Interview questions arise from the posting, and good answers come from the fact file. The model can generate a list of questions based on specific requirements, practice answers with you in the “situation, action, result” format, play a tough recruiter, and point out where the answer is general. Company research must have sources with dates, as the question “what do you know about our company” with an answer based on an invented fact ends the interview.
Same conversation. I have an interview at [COMPANY] for [POSITION], stage: [phone with recruiter / with manager / technical / panel / final]. Prepare me.
RETURN
1. Company research with sources and dates: what they do, what they announced in the last 6 months, who is on the team I am applying to (from public profiles), what problems are visible in postings and communications. Five facts, each with a link. No guesses.
2. Fifteen questions you expect at this stage, derived from the requirements in the posting and from gaps in prompt 3. For each: what the recruiter wants to check.
3. For the five toughest: an answer in the situation, action, result format, built solely from stories from the fact file, up to 90 seconds of speaking. Indicate where I lack specifics so I can fill in.
4. Questions about gaps and difficult topics: employment gap, career change, degree from another country, accent, visa. For each, a short, truthful answer that closes the topic and returns to value for the company.
5. Mock interview: ask me questions one at a time, wait for my answer, after each give a score from 1 to 5 and one correction. Be tough; do not praise generalities.
6. Eight questions I will ask: about the team, about the first 90 days, about why the position is open, about the decision process, about next steps and timelines.
7. Logistics: what to prepare (copies of the CV I sent, portfolio, questions), how to note the names of interviewers, when and what to send after the interview.
Check in the response: click on five facts about the company from point 1 before the interview. Practice answers from point 3 aloud with a timer; 90 seconds is less than it seems. In the mock interview, do not let the model ask all questions at once; it should wait for your answer.
Prompt 8 – Weekly Loop
Job searching is a process with numbers: how many applications, how many responses, from which channel, after how long. Without a table, you repeat a method that does not work. This prompt reads your table once a week and tells you what to change. When an offer comes, you read the contract according to the guide on analyzing employment contracts with AI.
New conversation in the project. I paste my application table from this week and previous ones (columns: date, company, position, channel: portal, company website, referral, recruiter, direct message; CV version; response: none, automatic rejection, rejection after interview, interview, offer; response date; notes).
RETURN
1. Numbers: applications, responses, and interviews broken down by channel, and the percentage of responses by channel. Median days to response.
2. What works, what doesn’t: which channel and which type of company gives interviews, which gives silence. If a portal gives zero responses on twenty applications, write that plainly.
3. Three changes for next week, each with a number (e.g., “five applications with a referral instead of twenty from the portal,” “two informational interviews”), and one thing I will stop doing.
4. Documents: whether the CV versions that got interviews differ from those that got silence; what this means for prompt 3.
5. Reminders: applications without responses from 5 to 7 days, for which I send one message; interviews after which I wait for a decision, and when to ask.
6. Status of the fact file: whether a new fact (project, certification, number) appeared this week that needs to be added.
7. One sentence: what is the biggest bottleneck at this moment (lack of responses, lack of interviews after the first stage, lack of offers after interviews) and what it says about where the problem lies.
TABLE:
[paste]
Check in the response: whether changes in point 3 are numbers, not advice. The bottleneck in point 7 indicates which prompt to repeat: lack of responses means prompts 3 and 6, lack of interviews after the first stage means prompt 7, lack of offers after finals means prompt 7 and sometimes the goal from prompt 1.
Appendix: Job Searching in Poland and from Poland
The scheme is the same, with a few differences. Polish portals and companies also use ATS, and recruiters search the database by words just the same. CVs in Poland are usually with a photo and a clause about personal data; consent clauses are not required for data processed for this recruitment, and the rules are explained by the Personal Data Protection Office. A person returning from the USA has an additional topic: how to describe American positions and companies so that a Polish recruiter understands them, and how to document experience. If you work from Poland for a foreign company, see the guide on remote work from Poland. For prompts 1, 3, and 4, add the block below.
I am looking for a job in Poland. Additional rules:
– Job titles and keywords should be taken from Polish postings; provide both Polish and English versions, as some companies advertise in English.
– CV: prepare a version according to Polish customs (order of sections, length, whether to add a photo, how to formulate information about personal data in accordance with GDPR without unnecessary clauses) and a neutral version without a photo. Indicate what cannot be required from the candidate.
– Experience from the USA: for each position, add one sentence explaining the scale and context (company size, market, equivalent Polish position); leave titles in English with the Polish equivalent in parentheses.
– Employment form in the posting: employment contract, B2B, commission; list what each means for me, and what to ask.
– Ranges: whether the posting states the salary; if not, how to ask.
– Form: questions about availability, form of cooperation, expectations in gross monthly; explain how to respond.
– If returning from abroad: documents confirming experience and education that the employer may require, and where to obtain them.
What AI Does Wrong When Job Searching
| Error | How It Looks | How to Catch It |
|---|---|---|
| Invented achievements | “Increased sales by 35 percent” without that number in the fact file | Reading the CV with the fact file next to it; crossing out every number without a source |
| The same verbs as everyone else | Spearheaded, leveraged, passionate, results-driven | Search the document; list of prohibited words in prompt 3 |
| Synonyms instead of words from the posting | “Project management” where the posting says “Project Management Professional” | Column “exact wording” in prompt 2 |
| Stuffing words | A list of thirty skills without context, white text | Every word in the context of a fact; nothing hidden |
| Format that does not parse | Two columns, icons, table with dates, header in graphics | Test copying to notepad |
| Invented facts about the company | “Recent award,” “latest implementation” without a link | Click each source before the interview |
| Invented codes and data | O*NET code, median salary, name of industry group from memory | Link to source with date |
| Matching score of 8 out of 10 for every posting | Flattery | “Evaluate strictly, like a recruiter with a hundred candidates” |
| “ATS score of 92 percent” from online tools | A number unrelated to how a specific system and recruiter search the database | Treat as a hint about words, not as a result |
| Untrue answers in the form | The model “optimizes” the answer about work eligibility | Truth; consequences extend beyond the application |
CV Format That Facilitates Reading
| Element | Yes | No |
|---|---|---|
| Layout | One column, top to bottom | Two columns, text boxes, sidebars |
| Section headers | Summary, Experience, Skills, Education, Certifications | “My journey,” icons instead of names |
| Positions | Title, company, city, month and year from and to, in separate lines or with a clear separator | Dates in a table, only year, “currently” without start date |
| Font and file | Standard font, PDF with a text layer or DOCX if the form requires it | Scan, image, unusual fonts, text in header and footer |
| Contact information | Name, city and state, phone, email, LinkedIn link, in the body of the document | Photo, date of birth, marital status, nationality, full address |
| Keywords | In the wording from the posting, in the context of a fact | List without context, white text, repetitions |
| Length | Usually one or two readable pages, according to relevant experience and instructions; federal CV maximum two pages | Artificial shortening at the cost of readability or ignoring the limit from the posting |
| File name | Name-Surname-Position.pdf | CV_final_v7_new.pdf |
Security and Privacy
- Do not enter your social security number, date of birth, document numbers, address, or bank details into the fact file or chat. No legitimate employer needs them before an offer.
- Do not pay for work. Fees for equipment, training, “verification,” or a check to cash are a scam pattern described by the FTC; communication solely through a messenger and an offer without an interview are signals of this.
- Do not send a CV with facts you cannot defend. Verification before hiring and the first weeks of work check them better than ATS.
Check model training settings in your account: Claude (Anthropic article), ChatGPT (OpenAI data settings). If you suspect that a rejection decision was discriminatory, candidate information is published by the EEOC. We discuss how to search for a job in the USA without fluent English in a separate guide, and about the first job in Chicago in a guide for Poles in Chicago.
Common Mistakes
- “Improve my CV” as the first prompt. Without a fact file, the model improves style and adds fabrications.
- One CV for a hundred postings. The recruiter’s search engine will not find words that are not there.
- A nice template. A complex layout can hinder reading. Not every process makes reading an attachment dependent on correct parsing.
- Tricks. White text, stuffing words, job title you did not have. Visible after parsing, costly in the interview.
- Only portals. Contacts and informational interviews are additional channels, with no guarantee of a separate queue.
- Lack of a table. Without numbers, you do not know what works, and repeat what does not work.
- Researching the company from the model’s memory. One invented “recent implementation” in the interview costs more than lack of research.
- Untruth in the form. Work eligibility, years of experience, degree: these are checked.
This scheme uses AI for research, matching, and practice, not for invention. Every CV, message, and response you send is yours, and you will defend it in the interview and at work. The model can generate a document that passes through the system but may not survive the first week in a new company. The fact file and the prohibition against making things up are more important in this guide than all the prompts combined.
Frequently Asked Questions
Does ATS really automatically reject CVs?
Some systems allow the employer to configure automatic rejection rules, e.g., based on answers to form questions. Other features support searching or matching without making decisions. An error in reading or silence after an application does not allow you to conclude that an algorithm rejected you.
Will a recruiter know that the CV was written by ChatGPT?
Based on individual words, authorship cannot be reliably determined. What matters more is whether the CV contains specific, true information and whether you can explain it in the interview. Follow the AI usage rules provided by the employer.
Do “check your ATS score” tools work?
A list of missing information can be helpful. A score generated by the model or an external scanner is not automatically the result of the ATS of a specific employer. Check justification and facts instead of optimizing just the percentage.
I have a diploma from Poland. What should I do with it?
Enter the name of the university, field, and degree. You can add a clear translation, but do not announce official equivalency of the diploma based on AI. If the posting or license requires evaluation, check the requirements and the appropriate institution; the NACES list is one of the sources.
How to respond to the question about visa sponsorship?
Truthfully, understanding that the question about work eligibility today and the question about the need for sponsorship in the future are two different questions. Prompt 4 breaks this down for your status; if in doubt, ask an immigration lawyer, not the model.
How many applications per week?
Fewer, better matched, with one person in the middle. Prompt 8 will show after two weeks which distribution between channels yields responses in your case.
Does this work for physical and service jobs?
Yes, with less emphasis on LinkedIn and more on people and places from prompt 6; many such companies recruit through referrals and local networks faster than through portals.
Fact-Check Summary
- Documented: ATS can provide file reading, searching, and form rules. The “Hidden Workers” report published in 2021 describes the problems of filters in the studied companies; its numbers should not be transferred to all employers in 2026.
- Practical recommendation: a simple layout and checking read fields limit some technical problems. A referral may help with contact, but the course of the process depends on the company.
- What is uncertain: how a specific system in a specific company sorts candidates; whether a given recruiter reads cover letters; what percentage of CVs the “robot” rejects (the number 75 percent circulates without a verifiable source).
- Common myth: “AI will write me a CV that will pass ATS.” It may pass; the problem starts in the interview if the CV contains things you did not do.
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