NLP Challenge

Project Overview

The Goal

The goal of this project was to build a reliable academic assistant that could answer questions directly from uploaded course content instead of relying on generic chatbot responses.

The project focused on improving study efficiency by allowing users to query learning materials in natural language and receive responses grounded in real documents.

The Solution

We approached this by building a Retrieval-Augmented Generation pipeline. Uploaded documents were parsed, chunked, and stored for retrieval. When a user asked a question, the system searched for the most relevant chunks before generating a response. To make the workflow more structured, we used LangGraph to model the application as a single agent with multiple steps for ingestion, retrieval, and answer generation.

Project Details

Timeline

Started

2025

Type

AI-powered academic assistant

Focus

Grounded answers from uploaded course materials

Collaboration

Group project by a team of 3. We worked on the ingestion, retrieval, generation flow, and testing together.

Technical Details

Software Used

VS Code, GitHub, FastAPI backend tooling

Technologies

Python FastAPI LangGraph Redis RAG PDF/PPTX/DOCX

Skills Applied

Soft Skills

Problem solving
System design thinking
Documentation
Iterative refinement

Technical Skills

Document ingestion and chunking
Semantic retrieval
Grounded generation
Agent workflow design with LangGraph

Key Features

Multi-format Ingestion

Supports PDF, PPTX, and DOCX files, converting them into searchable chunks for efficient retrieval.

Grounded AI Responses

Retrieves relevant content first, then generates more accurate and course-aware answers.

Educational Output Modes

Generates explanations, short summaries, and quiz-style questions for revision and active learning.

Agent-based Workflow

LangGraph manages ingestion, retrieval, and generation as a modular structured workflow.

Development Process

1. Research & Planning

Explored document ingestion, semantic search, and retrieval-based AI to define the architecture.

2. Design & Development

Built the backend with FastAPI, Redis-based retrieval, and LangGraph workflow orchestration.

3. Testing & Refinement

Improved chunking, retrieval relevance, and response flow to make answers more grounded.

4. Outcome & Learning

Delivered a working AI study assistant and deepened understanding of reliable AI application design.

The Challenge

Problem Statement

Traditional study materials are static and difficult to search efficiently. Students often spend too much time browsing slides or notes manually, and general-purpose AI tools may hallucinate or provide answers that are not grounded in course content.

The challenge was to create a system that could understand user questions, retrieve the right content from uploaded materials, and return answers that were accurate, relevant, and useful for learning.

Solution Approach

We approached this by building a Retrieval-Augmented Generation pipeline. Uploaded documents were parsed, chunked, and stored for retrieval. When a user asked a question, the system searched for the most relevant chunks before generating a response.

To make the workflow more structured, we used LangGraph to model the application as a single agent with multiple steps for ingestion, retrieval, and answer generation.

Key Takeaways

"This project showed me that building useful AI systems is not just about using a model. The real value comes from designing the full pipeline around retrieval, structure, and reliability."

System Design Insight

NLP + Backend Engineering

"Working on this project improved my understanding of document processing, semantic retrieval, and how agent-based workflows can make AI applications more maintainable and scalable."

Technical Growth

RAG + LangGraph + Redis

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