RAG + LLM Implementation | Building a Resume Analyzer with Chroma DB & Gemini | GenAI Series Ep 7

RAG Implementation | Build an AI Resume Analyzer using Gemini and ChromaDB. Learn how Retrieval-Augmented Generation (RAG), Vector Databases, Embeddings, and Semantic Search work together in a real-world AI application. Welcome to Episode 7 of the “Generative AI & Agentic AI” Series 🚀 In the previous episode, we explored the complete architecture of RAG systems, including offline indexing pipelines, online retrieval, generation layers, and maintenance workflows. Now it's time to move from theory to implementation and build a real-world RAG application 🔥 In this video, we will implement the RAG concepts that we have learned throughout this series by building an AI-powered Resume Analyzer using Gemini and ChromaDB. In this video, we will explore: 👉 Resume content extraction from PDF and DOCX files 👉 Building a RAG pipeline using static text data 👉 Creating embeddings from knowledge documents 👉 Storing embeddings in ChromaDB 👉 Retrieving relevant context using semantic search 👉 Integrating Gemini LLM with RAG 👉 Generating context-aware resume evaluations Topics Covered: 📌 Parsing resumes from PDF and DOCX files 📌 Building the offline indexing pipeline 📌 Loading and preprocessing knowledge documents 📌 Generating embeddings for ATS rules and resume guidelines 📌 Storing vectors in ChromaDB 📌 Querying the Vector Database 📌 Retrieving relevant chunks using similarity search 📌 Prompt augmentation using retrieved context 📌 Generating responses with Gemini 📌 Improving resume analysis using external knowledge Project Overview: This project is an AI-powered Resume Analyzer that extracts resume content and uses Gemini LLM to generate: ✅ Resume scores ✅ Eligible job roles ✅ Improvement suggestions To make the analysis more accurate and company-specific, we implement Retrieval-Augmented Generation (RAG) using ChromaDB, where ATS rules, resume guidelines, and job requirements are stored as embeddings. When a resume is uploaded, the system retrieves relevant knowledge from the Vector Database and provides a context-aware evaluation instead of relying solely on the LLM's general knowledge. By the end of this video, you will understand how to implement a complete RAG workflow and apply it to real-world AI applications. This video is beginner-friendly and highly useful for developers learning: ✅ Generative AI ✅ LLM Engineering ✅ RAG Implementation ✅ ChromaDB ✅ Google Gemini ✅ LangChain ✅ Enterprise AI Applications ✅ AI Resume Screening Systems Links: github link: https://github.com/the-systems-mind/Resume...