Resume Parsing Using Google GenAI – Capstone Project

In this blog post, we will discuss how we used Google GenAI to automate the resume parsing process. By leveraging GenAI's few-shot prompting and structured output generation, we can extract key information from resumes, such as the candidate's name, skills, and experience.

Problem

Manually reviewing resumes is a time-consuming task for recruiters. By automating this process, we can save valuable time and resources, enabling HR teams to focus on the most promising candidates.

Solution with GenAI

We used Google's Gemini-2.0-Flash model to parse resumes and extract structured data. The model was trained on a variety of inputs, making it capable of understanding and generating meaningful outputs in a structured format like JSON.

Here is a snippet of code showing how we use few-shot prompting for extracting structured data:

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Extract key information from this resume: [Insert Resume Here]"
)

Public Notebook

Link to My Kaggle Notebook
https://www.kaggle.com/code/aishakhan05/resumepar

Final Thoughts

This project showed how powerful Generative AI can be for solving real-world problems like resume screening. It combines AI, structured data, and human-like understanding into a seamless workflow.