Traditional RAG vs. Agentic RAG: A Comparative Study of Retrieval-Augmented Systems
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval to improve factual reliability. Traditional RAG employs a fixed, single-pass retrieval process, limiting its ability to handle multi
Authors
- Deepshikha Bhati (ORCID: https://orcid.org/0009-0002-0115-6026)
- Fnu Neha (ORCID: https://orcid.org/0009-0004-3702-2382)
Institutions
- Kent State University (US)
Publication Details
- Published
- 2025-08-26
- DOI
- https://doi.org/10.36227/techrxiv.175624551.12254549/v1
- Citations
- 1
- Primary Topic
- AI-based Problem Solving and Planning
- Type
- article
- Field-Weighted Citation Impact
- 2.48