When I started building ResumeMatcher, I wanted to create something smarter than a simple keyword matcher. The goal was to build an AI system that actually understands job descriptions and resumes — one that can explain why a candidate might be a great fit or where they fall short.
This idea led me to use a Retrieval-Augmented Generation (RAG) architecture — a technique that combines traditional information retrieval with the reasoning power of large language models (LLMs). In this post, I’ll explain how ResumeMatcher works under the hood and what I learned along the way.
Why RAG?
Traditional resume screening tools often rely on keyword frequency or rule-based systems. The problem is that these methods miss context — for example, they might flag “data scientist” and “data analyst” as completely different, even if the roles share overlapping skills.
RAG allows the system to bridge that gap. By combining semantic search with generative reasoning, it can retrieve the most relevant information from a large dataset and use an LLM to interpret and score it in a meaningful way.
Data Extraction and Preprocessing
Resumes and job descriptions come in many formats — PDF, Word, text — and with very inconsistent structures. The first step was to build a document loader that extracts text cleanly and handles layout issues like tables and multi-column formatting.
Once the text is extracted, ResumeMatcher cleans and standardizes it: removing headers, converting symbols, and normalizing spaces. This step is critical, since noisy text can drastically reduce the quality of embeddings later on.
Embeddings and FAISS Indexing
After preprocessing, the text is transformed into vector embeddings using OpenAI’s embedding models. These embeddings capture semantic meaning — words like “developer” and “engineer” end up close to each other in vector space.
The embeddings are then stored in a FAISS index (Facebook AI Similarity Search), which allows ResumeMatcher to perform high-speed similarity searches across thousands of resumes and job descriptions. This makes the matching process efficient, even on modest hardware.
The Matching Process
When a user compares a resume to a job description, ResumeMatcher retrieves the most semantically relevant sections from both documents using FAISS. These chunks of text are then passed, along with the original documents, to a large language model (GPT-3.5) through a carefully designed prompt.
The model analyzes both documents and produces:
A match score (0–100)
A short explanation describing the reasoning behind the score
(for example, “Strong alignment in data analysis and Python skills, but limited experience in cloud deployment.”)
This combination of quantitative and qualitative output makes the result more transparent and actionable than a simple percentage.
Building the Interface
ResumeMatcher provides both a command-line interface (CLI) for developers and a Streamlit web app for recruiters and job seekers. The web app visualizes match scores, explanations, and highlights key overlaps between documents, making it easier to interpret results quickly.
Lessons Learned
Building this system taught me several lessons:
Data cleaning is often more important than model tuning.
Embedding choice significantly affects retrieval quality.
Prompt design can make or break the accuracy of generated reasoning.
Even with a small dataset, thoughtful architecture and good preprocessing can deliver strong, reliable results.
Looking Ahead
What started as a personal project to simplify my job search has evolved into a tool that could help recruiters and candidates everywhere. My next goal is to improve multilingual support and experiment with domain-specific fine-tuning.
By combining retrieval, reasoning, and explainability, ResumeMatcher shows how AI can make hiring smarter, faster, and fairer — not by replacing people, but by helping them make better decisions.
Top comments (0)