Building Resume-Based Chatbots: A Creative Twist for Final Year Projects

Pushkar Nandgaonkar
May 9
3 min read

Imagine submitting a final year project that not only checks the boxes for innovation and technical skill but also boosts your placement prep. Welcome to the world of resume-based chatbots—smart assistants that can parse resumes or portfolios and answer relevant questions instantly. It’s a fresh, real-world application of AI that blends your academic goals with your career aspirations.

In this blog, we’ll guide you through how to build such a chatbot using Amazon Bedrock, LangChain, and Retrieval Augmented Generation (RAG). It’s simpler than it sounds, and a fantastic choice for students eager to impress professors and recruiters alike.

The Challenge: Traditional Resume Reviews Are Static

Resumes are often viewed as flat documents. Sure, they list skills and experiences, but they require human effort to interpret, analyze, and respond to queries. This creates a bottleneck, especially when recruiters sift through dozens of resumes or when students want instant feedback.

The Solution: A Context-Aware Chatbot

By using AI, we can turn resumes into interactive knowledge bases. A chatbot trained to understand a specific resume can answer questions like:

"What are this candidate's technical skills?"
"How many years of experience do they have?"
"What projects have they completed related to data science?"

This is made possible by combining three powerful technologies:

1. Amazon Bedrock

Amazon Bedrock offers easy access to top-tier foundational language models without managing infrastructure. It provides the AI brain for your chatbot.

2. LangChain

LangChain helps manage the logic behind the chatbot’s workflow—from question parsing to document querying.

3. Retrieval Augmented Generation (RAG)

RAG improves chatbot accuracy by pulling real-time, document-specific data before generating responses. That means your chatbot isn't guessing—it’s retrieving answers straight from the resume.

How to Build It: Step-by-Step

Here’s a simplified breakdown of the process:

Step 1: Upload the Resume Start by uploading a resume or portfolio to an Amazon S3 bucket. This becomes the source of truth for the chatbot.

Step 2: Ask a Question You (or a recruiter) ask the chatbot something like, "Does this candidate have experience with Python?"

Step 3: Use LangChain to Retrieve Context LangChain processes the question and uses RAG to search the document for relevant content.

Step 4: Generate the Answer Amazon Bedrock's model (e.g., Claude by Anthropic) uses the retrieved content to generate a human-like, accurate answer.

Step 5: Display the Response The chatbot displays the response in your interface—instantly and contextually.

Creative Academic Uses

This type of chatbot isn't just technically impressive—it has multiple academic and career-related applications:

Final Year Projects: Use it as a capstone project to demonstrate skills in AI, NLP, and cloud services.
Placement Preparation: Use the chatbot to analyze your own resume and help identify gaps or improvements.
Peer Review Tool: Let classmates upload resumes and get automated feedback.

Why Recruiters Will Love It

Imagine if a recruiter could simply ask, "What leadership experiences does this candidate have?" and get an instant answer. You’re not just building a chatbot—you’re building a bridge between data and decision-making.

Need Help Getting Started?

You don’t have to figure it all out alone. At CodersArts, we specialize in helping students build smart, customized AI solutions for projects just like this. Whether you need full development support or just guidance on tech stacks, we’re here to help you succeed.

You can also check out the project demo in the following