What is RAG Recruitment Assistant?
The RAG Recruitment Assistant is an innovative “Reverse Matching” system built on Retrieval-Augmented Generation (RAG) architecture. Unlike traditional recruitment systems that filter candidates by years of experience, this system focuses on identifying talent based on:Technical Potential
Evaluates candidates based on their skill stack, technical proficiency, and demonstrated abilities rather than tenure
Academic Projects
Values hands-on project experience, hackathon achievements, and academic work as key indicators of capability
Smart Matching
Uses AI to understand context and match candidates to roles based on semantic similarity, not keyword matching
Explainable Results
Generates natural language justifications for why each candidate is a good fit
The Reverse Matching Approach
Traditional recruitment systems filter candidates using rigid criteria like “5+ years of experience” or exact job title matches. The RAG Recruitment Assistant flips this paradigm:Reverse Matching means the system doesn’t ask “Does this candidate meet our requirements?” but instead asks “What valuable potential does this candidate have, and where would they excel?”
- Recent graduates with strong academic projects
- Career changers with transferable skills
- Self-taught developers with portfolio work
- Students seeking internships or entry-level positions
RAG Architecture Overview
The system implements a complete RAG pipeline optimized for recruitment:Document Ingestion
PDF resumes are loaded and parsed to extract text content, preserving structure and metadata
Vector Embedding
Text is converted into high-dimensional vectors using HuggingFace’s sentence-transformers, capturing semantic meaning
Vector Indexing
FAISS (Facebook AI Similarity Search) creates an efficient index for fast similarity search across thousands of candidate profiles
Semantic Retrieval
When a job requirement is submitted, the system finds the most semantically similar candidate profiles
Architecture Diagram
Technology Stack
The system is built with modern AI and ML tools:LangChain
Framework for building LLM-powered applications with modular chains and retrievers
FAISS
High-performance vector similarity search library from Meta AI Research
Gemini 1.5 Flash
Google’s fast and efficient large language model for generating natural language explanations
HuggingFace Embeddings
Sentence transformers for creating semantic embeddings from text
sentence-transformers- Neural network models for text embeddingspypdf- PDF document processingpandas- Data manipulation and analysisplotly- Interactive visualizations
Target Audience and Use Cases
Who Should Use This System?
University Career Services
University Career Services
Help match graduating students with internships and entry-level positions based on their academic projects, thesis work, and technical skills rather than non-existent work history.
Startup Talent Acquisition
Startup Talent Acquisition
Fast-growing companies that need to identify high-potential junior talent who can learn quickly, even if they lack traditional experience markers.
Technical Bootcamps & Training Programs
Technical Bootcamps & Training Programs
Organizations that want to demonstrate graduate outcomes by matching learners to opportunities based on portfolio projects.
HR Teams Focused on Diversity
HR Teams Focused on Diversity
Recruiters looking to reduce bias by evaluating candidates on demonstrated skills rather than pedigree or years of experience.
Primary Use Cases
- Internship Placement - Match students to internships based on academic projects and skill alignment
- Junior Role Screening - Identify entry-level candidates with the right technical foundation
- Project-Based Evaluation - Assess candidates by the complexity and relevance of their portfolio work
- Skills Gap Analysis - Understand what technical capabilities candidates possess beyond their job titles
Key Differentiators
What makes this approach unique:| Traditional Systems | RAG Recruitment Assistant |
|---|---|
| Keyword matching | Semantic understanding |
| Years of experience filter | Project complexity evaluation |
| Binary yes/no screening | Ranked candidates with explanations |
| Opaque decision-making | Transparent AI reasoning |
| One-size-fits-all criteria | Context-aware matching |
Project Context
This project was developed by Anghelo Mendoza Prado as a practical demonstration of applying Generative AI and RAG architecture to solve a real-world talent selection problem.The system was originally developed and tested in Google Colab, making it accessible to anyone with a browser and a Google API key. It can also run locally with Python 3.10 or 3.11.
What’s Next?
Ready to get started? Proceed to the Quickstart to run your first talent search, or jump to Installation if you want to set up a local environment.Quickstart
Get up and running in 5 minutes with Google Colab
Installation
Set up the system locally on your machine