RAG Evaluation Framework
Published in Red Hat, 2026
- Designed a comprehensive evaluation framework for Retrieval-Augmented Generation (RAG) systems to assess retrieval relevance, answer faithfulness, and hallucination rates.
- Built automated pipelines to evaluate retrieval quality using metrics such as context precision, recall, and ranking performance.
- Implemented LLM-based evaluation techniques for answer grounding, factual consistency, and response completeness.
- Developed benchmarking workflows to compare different retrievers, chunking strategies, and embedding models.
- Enabled systematic experimentation and monitoring to improve reliability of production RAG applications.