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ResumePilot is a small AI job-application copilot built to compare one resume PDF with a specific internship description. It organizes possible matches, gaps, keyword coverage and suggested next steps so an applicant can review what is relevant before applying. Its compatibility score is a feature, not a validated measure of hiring prospects.
Why ResumePilot was built
Project author Srijita Ghosh describes a friend repeatedly comparing the same resume with different internship listings. For each role, the applicant had to identify matching skills, spot missing requirements, find relevant experience and decide what to improve. ResumePilot puts those checks into one workflow rather than treating each application as a fresh manual comparison.
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The questions it is meant to help organize are practical: Which parts of my experience are relevant to this role? Which skills does the description emphasize? What is missing, and where is my resume too vague?
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The prototype accepts a resume PDF and a job description, then presents several dimensions of fit rather than only a single number.
#1 Best Overall
- Overall compatibility, with separate matches for skills, experience, projects and education.
- Strong matches and missing skills.
- Weak or underrepresented areas that may need clearer evidence.
- Keyword coverage categorized as Matched, Missing or Needs more context.
- Actionable recommendations that explain why a point may matter for the role.
That breakdown can give an applicant a checklist for reviewing their materials. It does not establish that the score is accurate, officially recognized by an applicant-tracking system, or predictive of an interview or job offer. The project description supplies no benchmark or controlled validation study.
How to use the feedback without misrepresenting experience
ResumePilot’s stated design principle is: “Improve the story your experience already tells. Don’t invent experience you don’t have.” A missing keyword should prompt a check for relevant, truthful evidence—not an instruction to add a skill the applicant lacks.
Rank #2
For example, if the tool flags a skill as missing or says a point needs more context, first ask whether a class, project, internship or other real experience supports it. If the connection exists, make the evidence more explicit in the resume. If the connection does not exist, leave the claim out. The author says suggestions without direct support may be marked “Verify before adding.”
Keyword coverage is framed as a way to find unclear evidence, not to stuff terms into a resume. That distinction matters: a job description can help identify what to explain, but it cannot make an unsupported qualification true.
How the described prototype works
According to Ghosh’s project description, the frontend uses React, Vite and Tailwind CSS; the backend uses Python and FastAPI. The stated processing path is:
- The applicant uploads a resume PDF and supplies a job description through the frontend.
- The FastAPI backend uses pypdf to extract text from the PDF and constructs a prompt from the resume text and job description.
- The prompt is sent to Gemma through the hosted Gemini API using google-genai.
- The response is extracted as JSON and validated as structured output with Pydantic.
- Keyword and score processing is applied, and the results are returned to the frontend.
The author says this implementation does not use retrieval-augmented generation, embeddings or a vector database. These are details from the project description, not an independent code review or performance assessment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the privacy description does—and does not—promise
Ghosh says the prototype does not store uploaded resumes on disk or in a database. However, it sends extracted resume text and the job description to a hosted API for inference; the described implementation uses Gemma through the Gemini API, not local inference. “Not stored by the app” therefore does not mean “never shared” or “processed locally.”
The Tool Desk
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What the project description cannot establish
- Whether the compatibility score accurately measures fit: no benchmark or validation study is provided.
- Whether using the tool improves interview or hiring outcomes: no such results are reported.
- Whether the demo is currently online, the repository is maintained, or the project is commercially available: the write-up does not establish current uptime, maintenance or pricing.
There is also a separate Devpost project with the same name. It describes a different team and system, so its features should not be attributed to Ghosh’s prototype.
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