Real Projects · Project 3
Build RAG App
Learn how to build a Retrieval-Augmented Generation application that answers questions using your own documents instead of relying only on the model's general knowledge.
Project Overview
A RAG app helps an AI model answer questions using external knowledge such as PDFs, documents, websites, policies, manuals, or internal company information.
Instead of asking the model to guess, the system retrieves relevant information first and then gives that information to the model.
This makes RAG one of the most important patterns in modern AI engineering.
What This Project Does
- Accepts documents or knowledge sources
- Splits documents into smaller chunks
- Creates embeddings for each chunk
- Stores embeddings in a vector database
- Accepts user questions
- Retrieves relevant document chunks
- Generates answers using retrieved context
Why This Project Is Useful
RAG apps are useful when users need answers from specific documents or private knowledge.
Companies use RAG for internal knowledge assistants, support bots, legal document search, HR policy assistants, product documentation, research tools, and enterprise search.
Core Architecture
- Document upload or ingestion layer
- Text extraction system
- Chunking logic
- Embedding model
- Vector database
- Question input interface
- Retriever
- LLM response generation
Suggested Tech Stack
- Python with FastAPI or Flask
- Next.js, React, or Streamlit for the frontend
- OpenAI, Claude, Gemini, or local LLMs
- Chroma, FAISS, Pinecone, Weaviate, Qdrant, or Milvus
- PDF/text extraction libraries
- Optional LangChain or LlamaIndex
Basic RAG Flow
- Upload documents
- Extract text from documents
- Split text into chunks
- Create embeddings
- Store embeddings in a vector database
- User asks a question
- Retrieve similar chunks
- Generate final answer with context
Important Design Decisions
RAG quality depends heavily on how documents are processed.
- Chunk size
- Chunk overlap
- Embedding model quality
- Retrieval strategy
- Prompt structure
- Source citation handling
Possible Improvements
- Add file upload support
- Add source citations
- Add multi-document search
- Add user authentication
- Add chat history
- Add reranking for better retrieval
- Add admin dashboard for documents
What You Learn
- Embeddings
- Vector databases
- Document processing
- Semantic search
- Prompt engineering
- AI architecture
- Retrieval-Augmented Generation
Summary
Building a RAG app is one of the best ways to understand practical AI engineering.
It teaches how AI systems combine documents, embeddings, vector databases, retrieval, prompts, and LLMs to produce more grounded and useful answers.