"How I Built a Smart Resume Reader Using RAG and LLM – A Step-by-Step Guide for Beginners"

June 28, 2025

"How I Built a Smart Resume Reader Using RAG and LLM – A Step-by-Step Guide for Beginners

"Ever thought of making an AI that reads resumes like a recruiter?

Well, I did exactly that — and in this blog, I'll show you how I built a Resume Reader using RAG (Retrieval-Augmented Generation) and LLM (Large Language Model) from scratch.

Whether you're an aspiring AI developer or someone building cool tools to automate HR tasks — this is a perfect project to level up your Python + AI skills.

🧠 What Is a Resume Reader LLM?

A Resume Reader LLM is a smart application that:

✅ Reads a resume (PDF/TXT)
✅ Understands the content using LLM
✅ Answers questions like “What is the candidate’s experience?”, “What are their top skills?”, etc.
✅ Uses RAG to improve accuracy by combining retrieval (searching your data) + generation (via LLM)

🧰 Tools & Tech Used

Python 3.10+
LangChain – to connect documents + LLM
OpenAI / HuggingFace LLM
FAISS – for vector similarity search
PyMuPDF or pdfplumber – to extract text from resume
Streamlit (optional UI)

🧱 Project Architecture (Resume Reader LLM with RAG)

             [User Query]
                  |
               [LangChain]
                  |
     -------------------------------
    |                               |
[Retriever (FAISS)]         [LLM (GPT)]
    |                               |
[Relevant Resume Chunks]   [Final Answer]
     \_____________________/ 
         RAG (Retrieval + Generation)

📦 Step-by-Step Tutorial

✅ Step 1: Install Required Libraries

pip install langchain openai faiss-cpu pdfplumber python-dotenv

✅ Step 2: Extract Text from Resume

import pdfplumber

def extract_text_from_pdf(pdf_path):
    with pdfplumber.open(pdf_path) as pdf:
        return "\n".join(page.extract_text() for page in pdf.pages if page.extract_text())

✅ Step 3: Chunk and Embed the Resume Text

from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

text = extract_text_from_pdf("resume.pdf")
splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
docs = splitter.create_documents([text])

db = FAISS.from_documents(docs, OpenAIEmbeddings())

✅ Step 4: Create the RetrievalQA Chain

from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=db.as_retriever()
)

query = "What are the candidate’s top 5 skills?"
response = qa_chain.run(query)
print(response)

🔎 Example Output

The candidate’s top 5 skills are:
1. Python programming
2. Machine Learning
3. SQL and data querying
4. Problem-solving
5. Team collaboration

💼 Real-World Use Cases

✅ HR automation – Quickly scan hundreds of resumes with custom questions
✅ Recruitment SaaS – Plug into job platforms as a smart screening tool
✅ College Projects / Hackathons – Impress with an applied AI solution
✅ Freelancing / Client Work – Offer AI-powered resume screening

🧠 Why RAG is Game-Changer Here

Normally, LLMs don’t “remember” PDFs — they hallucinate when the context is too big.
With RAG, we:

Break the resume into smart chunks ✅
Store in a searchable vector database (FAISS) ✅
Retrieve only the relevant parts for every query ✅
Combine with LLM for accurate and contextual answers ✅

It's like giving your GPT a personal memory — powerful, fast, and scalable.

⚡ Performance Tips

🔹 Use tiktoken to manage token limits
🔹 Use GPT-4 or Mistral-7B for better generation
🔹 Tune your chunk size to 300–700 tokens for resumes
🔹 Cache the FAISS DB so you don’t rebuild every time

🎯 Future Improvements

🌐 Add a Streamlit or Gradio UI
👤 Parse multiple resumes and compare candidates
📊 Build charts: skill heatmaps, experience timelines
🧩 Plug in other LLMs like Gemini, Claude, or Mixtral

🙌 Final Thoughts

This Resume Reader project was one of the coolest AI tools I built in a few hours. With RAG + LLM, you can build real-world apps that actually solve problems.
It’s just the beginning — imagine what else you could build: Document Q&A, Legal assistant, Custom ChatGPT for your data.

Want to try the code or collaborate?
🌐 GitHub: [your-link-here]
📬 Email/DM for custom tools or consulting.

🔚 TL;DR

You can build an AI Resume Reader using Python, RAG, FAISS, and GPT
It reads resumes and answers custom questions smartly
Perfect for HR automation, SaaS tools, or fun projects
RAG helps reduce hallucination and improve LLM accuracy

If you'd like me to turn this into a ready-to-publish markdown or create the video script + voiceover + shorts version — just say the word!

Would you like me to publish it on a platform like Medium or GitHub Pages for you?

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