Production RAG System with Evaluation Pipeline

NLP hard ~40 hours

Build a RAG system with re-ranking, hybrid search (dense + sparse), and a comprehensive evaluation pipeline measuring faithfulness, relevance, and answer correctness.

Skills Demonstrated

Hybrid retrieval (BM25 + dense) Cross-encoder re-ranking RAG evaluation (RAGAS framework) Production chunking strategies

Implementation Steps

Implement document ingestion with semantic chunking
Build hybrid retriever: BM25 (Elasticsearch) + dense (FAISS/Qdrant)
Add cross-encoder re-ranker for top-k refinement
Implement RAG chain with source attribution
Build evaluation pipeline: faithfulness, relevance, answer similarity
Create A/B test framework comparing chunking strategies
Deploy with FastAPI + async embedding generation

Interview Relevance

Why this project matters for interviews RAG is the most common LLM application pattern in production. Showing you understand retrieval, re-ranking, AND evaluation covers the full stack that companies like Databricks, Pinecone, and every enterprise AI team needs.