calhacks-10

Inspiration

Our team is fighting night and day during this recruitment season to internships. As with many others, we have varied interests in the fields that we specialize in, and we spend a lot of time tailoring our Résumes to the specific position we are looking at. We believe that an automatic Résume will immensely simplify this process.

What it does

The program takes two inputs: (1) our CV, a full list of all the projects we've done and (2) The job description of the job posting. The program will then output a new compiled resume that contains rewritten information that is most relevant to the position.

How we built it

We designed this project to utilize the most recent AI technologies. We made use of word2vec to create word embeddings, which we store in a Convex database. Then, using Convex's built-in vector search, we compare the job postings with your list of projects and experiences, and output the 5 most relevant ones. Finally, as a last measure, we run the projects through an LLM to shape them to be a good fit for the job description.

Challenges we ran into

We had a lot of challenges making in handling the difference cases of resumes and parsing the differences in it.

What we learned

We learned all sorts of things from this project. Firstly, the power of vector embeddings and their various use cases with all sorts of media. We also learned a lot regarding the space of ML models out there that we can make use of. Lastly, we learned how to quickly run through documentations of relevant technologies and shape them to our needs.

What's next for resumebuilder.ai

We managed to get the nearest work summaries that is associated with the job. Next, we plan to rebuild the resume such that we get pdfs formated nicely using latex