Building a RAG-Powered Research Agent
Project Description
This project introduces you to the cutting edge of applied AI, where you’ll design and build a RAG-Powered Research Agent capable of answering questions grounded in real scientific literature. RAG (Retrieval-Augmented Generation) is a technique used to combat a major weakness of Large Language Models: hallucination. Throughout this project, you’ll learn how to retrieve and process academic papers from arXiv, chunk and embed text into a vector database, and wire everything together with Claude as your LLM backbone. You’ll build each component from scratch, from the arXiv connector to semantic search with FAISS, and end with a fully interactive Q&A system that cites its sources. By the end, you’ll understand both the theory and the practical engineering behind modern AI systems. This project is ideal for those curious about how tools like ChatGPT or Perplexity actually work under the hood.
Dataset
This project will guide you in querying live research paper data from the arXiv API — a free, open-access repository of scientific papers in fields like AI, physics, and mathematics. No dataset download is required; the agent fetches paper summaries in real time based on your search queries.
Relevant Skills You May Apply
Basic Python Programming, familiarity with APIs, introductory understanding of Machine Learning or NLP concepts
Skills You May Gain
Retrieval-Augmented Generation (RAG), Vector Embeddings and Semantic Search, Large Language Model Integration, Prompt Engineering, Document Chunking and Processing, FAISS Vector Databases, API Interaction (arXiv + Anthropic Claude), System Design for AI Pipelines
Total Time
Approximately 10 to 20 hours (2 to 4 weeks)
Milestones
Milestone 1: Understanding RAG — Theory and Architecture
Milestone 2: Setup — Installing Libraries and Configuring the Claude API
Milestone 3: Building Components — ArXiv Connector, Document Processor, and Embeddings
Milestone 4: Running the Agent — End-to-End Pipeline Execution
Milestone 5: Evaluating the RAG System — Testing, Reflection, and Performance Analysis
Deliverables
Deliverables include a project report highlighting new skills gained and an interactive Python notebook (Google Colab)