Mastering LlamaIndex: From Fundamentals to Building AI Apps/

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Job Application Optimizer with LlamaIndex

Job Application Optimizer with LlamaIndex

Learn how to extract structured information from resumes and use LLMs to generate personalized feedback based on job descriptions.

We'll cover the following...

In this lesson, we will build an AI-powered assistant that helps candidates tailor their resumes for specific job applications. The system will extract structured information from a resume—such as work experience, education, and skills—and use this data to evaluate how well the resume matches a given job description.

Based on this analysis, the assistant will generate personalized suggestions for improving the resume. This may include highlighting relevant experience, identifying missing keywords, or recommending clearer skill positioning.

This type of application is useful for job seekers, career coaches, or platforms that provide resume screening and enhancement features.

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Application interface
Application interface

Note: This system uses structured data extraction to parse resumes and job descriptions, memory to maintain context across turns, and prompt construction to generate personalized application content.

To implement this application, we’ll use the following modules and libraries:

Modules and Libraries

Library/Module

Purpose

LlamaIndex

Structured data extraction and LLM interaction

Streamlit

Front-end interface for user input and result display

Groq

LLM backend to generate feedback and improvement suggestions

Pydantic

Define and enforce schema for structured resume fields

Setting up the Streamlit interface

We’ll use Streamlit to create a simple interface that accepts two inputs from the user:

  1. A resume PDF file: This will be parsed and converted into structured fields using a predefined schema.

  2. A job description: This can be pasted as free-form text.

We’ll also add a button to trigger the analysis process, which will extract structured data from the resume and generate improvement suggestions using a language model.

We build a user interface with the following code:

import streamlit as st
# Page config and title
st.set_page_config(page_title="Job Application Optimizer")
st.title("🎯 Job Application Optimizer")
st.markdown("Upload your resume and paste a job description to get personalized feedback.")
# File uploader for resume
uploaded_resume = st.file_uploader("📄 Upload your resume (PDF)", type=["pdf"])
# Text area for job description
job_description = st.text_area("📝 Paste the job description")
# Action button to trigger analysis
analyze_clicked = st.button("Analyze Resume")
Set up the Streamlit interface to accept a resume file and job description text, and provide a button to run the analysis

Next, we’ll define the ...