RAG system with evaluation and drift monitoring

A project template for retrieval-augmented generation with vector search, hallucination checks, automated evaluation, and monitoring.

System Goal

Build a retrieval-augmented generation system that answers domain-specific questions using a controlled document collection, with explicit evaluation for retrieval quality, groundedness, and answer usefulness.

Why This Demonstrates Expertise

A credible RAG project is more than a chat interface. It should show how documents are chunked, embedded, indexed, retrieved, reranked, cited, evaluated, monitored, and improved.

Architecture

Planned components:

• Document ingestion and cleaning.
• Chunking strategy and metadata schema.
• Embedding model selection.
• Vector database, such as FAISS or Pinecone.
• Retrieval and reranking.
• Response generation with citation constraints.
• Automated evaluation dataset.
• Hallucination and groundedness checks.
• Drift and regression monitoring with LangSmith or Langfuse.

Evaluation Plan

Track:

• Retrieval recall at k.
• Context precision.
• Citation support.
• Faithfulness or groundedness.
• Answer relevance.
• Latency and cost.
• Regression performance across prompt, model, and retrieval changes.

Notebook Plan

• notebooks/rag-evaluation-system/01-ingestion.ipynb
• notebooks/rag-evaluation-system/02-embeddings-and-index.ipynb
• notebooks/rag-evaluation-system/03-generation.ipynb
• notebooks/rag-evaluation-system/04-evaluation.ipynb
• notebooks/rag-evaluation-system/05-monitoring.ipynb

Executive Summary Template

Replace this with a concise system readout: what the system does, where it performs well, where it fails, and what monitoring is required before deployment.