Project Overview
My first hands-on RAG project — a PDF Q&A chatbot (LangChain + HuggingFace) that lets you upload a document and ask questions grounded in its actual content, built alongside a simple Groq-powered chat agent to learn the retrieval-augmented-generation fundamentals end to end.
Project_Type
RAG_LEARNING_PROJECT
Entry_Year
2025
Target_Platform
WEB
Primary_Role
AI_ENGINEER
Deployed_Technologies
Color_Palette
Project Walkthrough
A closer look at the finer details of this case study. Explore the core features, thoughtful design decisions, and interactive moments that shape the user experience.
[ Page_Index ]
- 01 // The Challenge
- 02 // Interactive Gallery
- 03 // The Solution
01 // The Challenge
The Challenge
This was my first hands-on RAG project — the goal wasn't production polish, it was understanding how retrieval-augmented generation actually works end to end: chunking a document, embedding it, retrieving the relevant pieces, and grounding an LLM's answer in them instead of letting it hallucinate.
02 // Interactive Gallery
03 // The Solution
The Solution
Built a PDF Q&A chatbot (LangChain + HuggingFace) that lets you upload a document and ask natural-language questions grounded in its actual content, plus a separate lightweight chat agent (Groq + Python) to get comfortable with the basic LLM request/response loop before building anything more complex.
Key_Contributions
Built my first end-to-end RAG pipeline — PDF upload, chunking, embedding, and retrieval-grounded Q&A with LangChain and HuggingFace — plus a simple Groq-powered chat agent, both as hands-on learning projects to nail the fundamentals before building the more complex agent systems that followed.
Next_Project
Dispatch Planning & Warehouse Optimization