What is RAG and Why It Matters
Retrieval-Augmented Generation (RAG) is an AI architecture pattern where a language model retrieves relevant documents from a knowledge base before generating a response. Instead of relying solely on training data, RAG grounds answers in your actual documents, databases, and knowledge sources. This dramatically reduces hallucinations and makes AI outputs verifiable. For enterprises, RAG is the bridge between powerful language models and trustworthy, domain-specific AI applications.
Map Query Classes Before Selecting Retrieval Strategy
Not all questions are the same. A factual lookup ('What is our refund policy?') requires different retrieval than a synthesis question ('Summarize Q3 performance across all regions'). Before building, categorize the types of queries your system will handle. Factual queries need precise, single-document retrieval. Analytical queries need multi-document aggregation. Conversational queries need context window management. Each query class may need a different chunking size, embedding model, or retrieval algorithm. One-size-fits-all RAG architectures underperform because they optimize for one query type at the expense of others.
Track Answer Faithfulness and Citation Quality
The most important RAG metric is faithfulness — does the answer actually reflect what the retrieved documents say? Build evaluation suites that compare generated answers against source documents. Track citation accuracy: when the model claims something comes from a specific document, verify that the document actually contains that information. Run these evals on a representative set of questions regularly, not just during development. Quality degrades silently as knowledge bases grow and change, so continuous evaluation is essential.
Add Fallback Paths When Confidence Is Low
RAG systems should know when they do not know. When retrieval confidence is low — measured by embedding similarity scores, re-ranking scores, or answer consistency across multiple retrievals — the system should not guess. Instead, implement fallback paths: surface the most relevant documents without a generated answer, escalate to a human reviewer, or ask the user to rephrase. These fallback paths build trust and prevent the catastrophic confidence failure where users stop trusting the system after a single bad answer.
Production RAG Architecture Considerations
Production RAG requires more than a vector database and an LLM API call. Consider: (1) Ingestion pipelines that handle document updates, deletions, and format variations. (2) Chunking strategies optimized for your document types — code documentation needs different chunking than legal contracts. (3) Hybrid retrieval combining semantic search with keyword matching for better recall. (4) Re-ranking models that improve precision after initial retrieval. (5) Caching layers for frequently asked questions. (6) Cost controls — embedding and LLM calls add up at scale. (7) Monitoring for retrieval quality drift over time.