Enterprise RAG Assistant
Build a document-aware AI assistant using RAG, embeddings and vector search, then deploy it with Streamlit.
Build a document-aware AI assistant using RAG, embeddings and vector search, then deploy it with Streamlit. Implement it step by step, deploy it, publish it on GitHub and prepare to explain it confidently in interviews.
Digital product · 12 months access · Non-refundable after purchase
AI Research Assistant
Complete Source Code
Clean and documented
Deployment Guide
Step-by-step process
Resume Points
Ready-to-use for your resume
Interview Questions
With detailed explanations
What You'll Learn
Gain practical experience building a production-ready AI system, from architecture to deployment.
- Core RAG concepts and when to apply them in real projects
- Practical RAG implementation techniques
- Practical Embeddings implementation techniques
- Practical LangChain implementation techniques
- Structuring a clean, production-style Python project
- Building an interactive Streamlit application
- Deploying the application to the cloud
- Explaining the project architecture confidently in interviews
Technologies Used
- Python
- RAG
- Embeddings
- LangChain
- Streamlit
- Cloud Deployment
Project Architecture
Understand how the complete system components work together.
Ingestion
Embedding Layer
Knowledge Store
Application Layer
Generation
Output
Project Demo
See the final application in action.
Preview of the final application you'll build.
- 3 Lessons · ~45 Mins
- 1. Project Overview15 min
- 2. Real-World Use Cases8 min
- 3. Final Architecture Walkthrough15 min