How to Tailor a Resume to a Job Description with AI
A worked AI resume-tailoring example: compare one job posting with a candidate’s experience, confirm missing details, and leave unsupported requirements out.
A job description can reveal experience missing from your resume. It can also name work you haven’t done. Before accepting an AI suggestion, distinguish those two cases.
This example follows one fictional candidate applying to a fictional analytics engineering role. The posting, resume, and follow-up answers are supplied below so every edit can be checked against them.
Priya’s current resume
Priya Raman — Data Analyst
Summary: Data analyst with four years in e-commerce reporting and analysis.
Analyst, Kestrel Retail · 2023 – Present
- Builds the weekly trading report in SQL for the merchandising team
- Maintains the Looker dashboards used by category managers
- Investigates data discrepancies raised by finance
Junior Analyst, Kestrel Retail · 2022 – 2023
- Ran ad-hoc queries for the marketing team
- Automated the monthly sales extract, replacing a manual export
Marketing Assistant, Halden Group · 2021 – 2022
- Produced campaign performance summaries in Excel
Skills: SQL, Excel, Looker, Data analysis, Reporting
Education: BSc Economics, 2021
The target posting
Analytics Engineer — subscription business, hybrid
Build the transformation layer used by the analytics team. Model raw event data into tested, documented datasets and support self-service reporting.
Requirements: Strong SQL; dbt or similar transformation tooling; familiarity with a cloud warehouse such as Snowflake, BigQuery, or Redshift; Git and code review; experience building and maintaining BI dashboards.
Nice to have: Python, Airflow or another orchestrator, A/B testing and experiment analysis, previous work in a subscription or SaaS business.
The posting has five requirement groups. Priya’s original resume supports SQL and dashboard maintenance. It doesn’t say whether she has built dashboards, used transformation tools or a cloud warehouse, or worked with Git and code review.
Follow-up answers before editing
Priya checks her work records and confirms:
- She writes the SQL for the trading report and maintains its column definitions. She has not designed a reusable transformation layer or defined a data model.
- She commits SQL changes to a shared Git repository and reviews colleagues’ pull requests.
- Category managers in four merchandising teams use the Looker dashboards.
- She has not used dbt, Airflow, Python, or A/B testing at work, and has no relevant project experience with them to add.
- She has not worked in a subscription or SaaS business.
- She has not established which warehouse the team uses or which warehouse-specific tasks she has performed.
Finding out that Looker connects to BigQuery would identify the team’s infrastructure. It would not, by itself, establish Priya’s experience with BigQuery. She needs to check what she actually queried, configured, or maintained.
Match terms to evidence
| Term or requirement | Evidence | Decision |
|---|---|---|
| SQL | Reports, queries, and the monthly extract | Include |
| Building and maintaining BI dashboards | Maintains Looker dashboards; creation not confirmed | Include maintenance; check creation separately |
| Documentation | Maintains the report’s column definitions | Add this specific task |
| Git and code review | Commits SQL and reviews pull requests | Add |
| Data modeling | Report writing is confirmed; model design is not | Don’t relabel the work as modeling |
| dbt or similar transformation tooling | No relevant experience confirmed | Leave out |
| Cloud warehouse | Product and tasks not yet established | Leave out pending verification |
| Python | No supporting experience supplied | Leave out |
| Airflow | No supporting experience supplied | Leave out |
| A/B testing | No supporting experience supplied | Leave out |
| Subscription / SaaS | Retail experience does not establish this | Leave out |
The follow-up establishes SQL and Git with code review. The dashboard requirement is only partly covered: maintenance is confirmed, but creation still needs checking. Documentation adds relevant detail; it doesn’t establish experience designing data models or a transformation layer.
Use the same keyword-to-evidence check when a suggested term sounds close to something you’ve done. Similar work can be transferable without being identical.
What the AI workflow adds
In ExportMyResume, paste the description into Tailor to a job posting, then run Improve with AI. The resume and pasted posting text are sent for processing. The optional job URL is saved with the version; the tool doesn’t fetch the posting from that URL.
A fit check can proceed with tailoring, ask for confirmation of additional experience, or offer general cleanup when the role is unrelated. Treat any experience questions as factual questions. Selecting an item can cause it to appear in the rewrite.
The editor also shows missing keyword chips and a tally of terms present in the resume. These are text matches. They do not establish the candidate’s qualifications or predict an employer’s decision. Check each chip against the evidence before adding it to Skills or Tools.
AI requires sign-in and has a daily run limit, with more runs on Pro. Saving multiple versions requires Pro as well. Free for now during the launch offer: keep multiple versions in the editor without an account or subscription; sign in to sync them to your account.
The tailored text
Priya keeps her actual job title and describes the relevant work more precisely:
Summary: Data analyst with four years in e-commerce reporting and analysis. Builds SQL reports, maintains Looker dashboards, and works in a shared Git repository with pull-request review.
Analyst, Kestrel Retail · 2023 – Present
- Builds the weekly trading report in SQL and maintains its column definitions
- Maintains Looker dashboards used by category managers across four merchandising teams
- Commits SQL changes and reviews colleagues’ pull requests in a shared Git repository
- Investigates data discrepancies raised by finance
Skills: SQL, Excel, Looker, Git, Data analysis, Reporting
The first bullet gains a confirmed documentation task. The second gains a confirmed team count. The third adds work omitted from the original. No tool from the posting is added merely because it appears there.
The junior analyst’s automation bullet can stay unchanged. The marketing assistant role can remain a compact entry if space is limited. Editing those older lines won’t fill the transformation-tooling or warehouse gaps.
Decide what to do with the gaps
Priya can investigate the warehouse work and assess whether the employer accepts adjacent SQL experience in place of dbt. The posting may allow flexibility, or the requirements may be firm. The number of matched terms doesn’t answer that question.
If she applies, she can describe the SQL reporting work accurately and explain her interest in transformation tooling without claiming prior dbt experience. A cover letter might say: “My current work centers on SQL reporting and dashboard maintenance. I haven’t used dbt; I’m interested in moving into tested transformation pipelines.” She should add any learning or project detail only after doing it.
Save and check the application version
Keep the source resume so the next application doesn’t start from a document already narrowed to this one. A master resume and separate application versions can make that easier. Use the editor’s free versions during the launch offer, or keep the master in a separate document.
Before exporting, compare names, dates, tools, and numbers with the source and follow-up answers. Read each rewritten bullet for changes in responsibility as well as wording. PDF, Word, and PNG without a watermark are free after sign-in during the launch offer; plain text and JSON backups are free without an account.