Production RAG Chatbot
Intermediate⏱ 5–7 days · 7 milestones
Build a chatbot that answers questions from your own documents using RAG — chunking, embeddings, vector search, and LLM generation with source citations.
Difficulty
Intermediate
Duration
5–7 days
Milestones
7 steps
Interview Qs
4 questions
🛠 Tech stack
✅ Prerequisites
- ● Python (intermediate)
- ● Basic ML concepts
- ● REST APIs
Step-by-step milestones
Document ingestion pipeline
Load, clean, and chunk documents.
Generate embeddings
Embed chunks using OpenAI or sentence-transformers.
Set up vector DB
Store and query embeddings in Qdrant or Pinecone.
Build retrieval + generation
Retrieve top-K chunks and feed to LLM.
Add source citations
Return source document name and page with each answer.
Evaluate quality
Measure retrieval precision and answer faithfulness.
Deploy
Containerize with Docker and deploy to cloud.
Skills you'll build
📝 Interview questions you'll face
- 1.What chunking strategy did you use and why?
- 2.How do you handle questions outside your knowledge base?
- 3.How do you evaluate RAG pipeline quality?
- 4.What is the difference between dense and sparse retrieval?
🌟 Portfolio guidance
Build it on a real corpus (company docs, research papers). Write a blog post explaining your chunking + retrieval choices.