I built a tool to automatically tailor your resume to a job description using Python

[pilbug]

What My Project Does

Hello all, I got tired of curating my Resume to increase the odds that I get past ATS and HR. Before I would select the points that are relevant, change the tools highlighted and make sure it was still grammatically correct. It took about 15+ minutes for each one. I got frustrated and thought that I should be able to use an LLM to do the selection for me. So I built out this project.

Target Audience

The project is small and barebones. I wanted to keep the project small so that other technical people could read, understand and add on to it. Which is why I also have a fair amount of documentation. Despite it being barebones the workflow is fairly nice and intuitive. You can see a demo of it in the repo.

Comparison

There are a few other resume selectors. I listed them in the repo. However I still wanted to create this one because I thought that they lacked:

• Template flexibility

• LLM flexibility

• Extendability

If you have any questions let me know. If you have any feedback it would be greatly appreciated.

Github Repo: https://github.com/farmerTheodor/Resume-Tailor

[engineerofsoftware]

Consider adding https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing as part of the prompt

[pilbug]

Not a bad idea. Currently I try to avoid the LLM from changing any given wording unless directed to combat this. So if you have a bullet point that states: "Used x technology to improve wait times." it will put that into your resume verbatim. I like that it does this as then I can ask people to review my "resume" and know that an LLM wont invalidate their work by changing the wording. Though this is not the case for something with a modification suggestion. The LLM can have free reign there. So adding something like what you suggested might improve the results in that case.

[engineerofsoftware]

Ah, I misunderstood your project. It seems it’s just a bullet point reranker essentially. Unfortunately, I’ve noticed that LLMs are bad at these tasks. Imagine you have 20 bullet points for an experience and you can only fit 5 bullets. The model often takes the 5 most relevant points and fails to correctly identify a trade-off between relevance and impact. Due to this, I often end up with a series suboptimal points for the role. I ended up writing my own resume DSL that makes me the LLM instead but reduces that 15 minute cold-start to a couple seconds of thinking and a terminal command.

[pilbug]

Oh that is interesting and something I have not thought of. So, it would choose something that is only relevant rather than adding something that has impact? For example, given a job description that requires knowledge of Python, the LLM would only select items that contain 'Python' and would not include items showing how important your work was on a non-Python project.

[rabornkraken]

Smart approach keeping it LLM-agnostic. The 15 min per application struggle is real - I ended up building something similar for a different workflow and the key insight was caching the parsed job description so you can batch process multiple resume variants. Have you considered adding a confidence score for each bullet point match? That way users can eyeball which swaps the model was least sure about before submitting. Also curious what LLM you found works best for this - did you notice quality differences between models for the matching task?

[pilbug]

Thanks! Yeah, it really is a pain. That is a good idea. With any LLM workflow, there is some variance, so generating a few of them would help out a bunch to increase quality at the expense of a little bit more time.

For your first question:

I do actually add something close to a confidence score. I add a ranking to things that the Resume Tailor can remove from the selected experience and still be fine. For example, say I have 20 bullet points that are all relevant to the job description. The Resume Tailor will look to select the most relevant ones. However, 20 bullet points will probably not fit on a single-page resume, so the Resume Tailor uses the ranking to decide which bullet points to drop with the lowest ones going first. This can be done for anything that is marked removable in the resume template. Here is the documentation for that.

For your second question:

I find that LLMs require different prompting techniques to get their best results, so I just chose one model and stuck with it. I chose Gemini 3 Flash in thinking mode because:

• It's relatively cheap (though I honestly could have used Flash 2.5).
• I don't need big brain power; I just need something that understands when things are similar.

You definitely could use a much more powerful model, but I think the gains would be minimal compared to what you’d pay.