Retrieval-Augmented Generation: Enhancing AI with Reliable Knowledge.

Raja Patnaik

Abstract: Retrieval-Augmented Generation (RAG) bridges the gap between large language models (LLMs) and enterprise knowledge. While LLMs are powerful, they often generate inaccurate or outdated responses due to static training data. RAG solves this by retrieving relevant information from structured and unstructured knowledge sources, augmenting prompts, and then generating contextually accurate answers. This paper introduces RAG?s core concepts, explains how it works, discusses best practices for implementation, and explores its role in grounding AI for trustworthy and scalable enterprise applications.

Keywords: Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Semantic Search, Hybrid Search, Vector Search, Knowledge Grounding, Prompt Engineering, Enterprise AI, Agentforce, Trustworthy AI, Knowledge Management

How to Cite?: Raja Patnaik, "Retrieval-Augmented Generation: Enhancing AI with Reliable Knowledge.", Volume 14 Issue 11, November 2025, International Journal of Science and Research (IJSR), Pages: 124-128, https://www.ijsr.net/getabstract.php?paperid=SR251104092705, DOI: https://dx.doi.org/10.21275/SR251104092705