AI & Machine Learning
Retrieval-Augmented Generation (RAG) is an AI architecture that retrieves relevant documents from a knowledge base before generating an answer with a large language model. It grounds responses in your real data, reduces hallucinations, and lets you cite sources for every answer.
At a glance
Definition
Retrieval-Augmented Generation, or RAG, is the dominant pattern for building production AI systems on top of proprietary or fast-changing data. Instead of relying on the static knowledge baked into a language model at training time, a RAG system fetches the most relevant passages from a maintained corpus and passes them to the model as context: so the answer is grounded in evidence, traceable to a source, and always as fresh as the underlying documents.
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A RAG pipeline has four stages: ingestion, retrieval, generation, and evaluation. Ingestion chunks documents and writes them to a vector database alongside metadata. Retrieval converts the user's query into an embedding and pulls the top-k chunks by similarity, often combined with keyword filters and a reranking model. Generation passes the retrieved context plus the original question to a language model, which produces an answer with inline citations. Evaluation measures answer correctness, retrieval recall, and faithfulness against ground-truth datasets, and runs continuously as the corpus evolves.
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RAG solves three problems that block enterprise AI deployments. It eliminates the need to fine-tune on private data for most use cases: cheaper, faster, and updatable without retraining. It produces auditable answers, because every claim can point back to the source document. And it stays current: when a policy changes or a product launches, you re-index, not re-train. For regulated industries (healthcare, financial services, legal), RAG is the only path that gives compliance teams the audit trail they need.
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Naive RAG implementations fail at scale. Embedding everything with one model rarely beats hybrid retrieval (dense + sparse + filters). Skipping a reranking step costs accuracy on tail queries. Measuring retrieval quality only on synthetic data masks production drift. And without a continuous evaluation harness, regressions on edge cases ship silently. The model returns confident-sounding wrong answers, and trust erodes.
Approach
Every Prosigns RAG engagement starts with retrieval evaluation, not prompt engineering. We build the eval harness against your real corpus before picking a vector store. We ship hybrid retrieval, citation tracking on every answer, and continuous quality monitoring. The CORTEX department leads this work, with FOUNDATION (data engineering) handling ingestion pipelines and CITADEL (security) handling data sovereignty for regulated workloads.