RAG System Optimization Auditor

Audit my RAG (Retrieval-Augmented Generation) system and suggest optimizations: Current setup: - Document type: {{document_type}} - Chunk size: {{chunk_size}} - Embedding model: {{embedding_model}} - Vector DB: {{vector_db}} - Retrieval method: {{retrieval_method}} Please analyze and provide: 1. **Chunking Strategy**: Is my chunk size optimal? Suggest better strategies (semantic, recursive, parent-child) 2. **Embedding Quality**: Rate my embedding model choice and suggest alternatives 3. **Retrieval Improvements**: Hybrid search, re-ranking, query expansion techniques 4. **Context Window Optimization**: How to pack the most relevant context 5. **Evaluation Framework**: Metrics to measure retrieval quality (MRR, NDCG, faithfulness) 6. **Common Failure Modes**: What typically goes wrong and how to fix it 7. **Code Snippets**: Implementation examples for top 3 recommendations