Retrieval Augmented Generation RAG configuration

Retrieval Augmented Generation RAG configuration is the set of tunable parameters that shapes how a RAG pipeline finds knowledge and feeds it to a large language model. It spans four layers: data prep (chunk size, overlap, embedding model, metadata), retrieval strategy (vector or hybrid search, filters, rerankers), generation context (prompt template, token budget, citation style), and orchestration logic (fallback LLMs, confidence thresholds, caching, security). Engineers adjust these levers to trade off latency, accuracy, and cost. Larger chunks boost semantic coverage but risk context overflow; hybrid BM25-plus-vector search improves recall at the expense of compute. A robust configuration also defines evaluation metrics—precision, citation precision, hallucination rate—and iterates via automated A/B tests. Version-controlled YAML or JSON files store the settings so teams can reproduce builds, roll back quickly, and swap vector databases or models without code rewrites, turning RAG from an experiment into maintainable production software.