Plug In Your Company’s Brain: Retrieval‑Augmented Generation Slashes Hallucinations and Keeps LLMs Fresh—Without Costly Retraining
For teams taking AI from demo to dependable, Retrieval‑Augmented Generation (RAG) is proving to be the pragmatic shortcut: pair a large language model with search over your own documents, and you can ground answers in sources, cut hallucinations, and update knowledge in minutes instead of waiting for retraining cycles. According to Retrieval‑Augmented Generation for Knowledge‑Intensive NLP Tasks, combining a neural retriever with a generator improves performance on open‑domain QA benchmarks by letting models cite evidence rather than rely solely on parametric memory. Dense Passage Retrieval shows that better retrieval lifts end‑to‑end QA, while REPLUG demonstrates gains even when the LLM is a sealed black box. New directions—like dynamic knowledge‑graph attention and multimodal hybrid retrieval—expand RAG into real‑time, regulated, and domain‑specific workflows.