ISSN2320-2882
Impact Factor9.17
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ESTD2013
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Paper Title
Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models.
Authors
RITESH PRAVIN PAITHANKAR , BHAKTI BHAGWAN KOR , TULSI PANDHARINATH PATIL , NIRAJ ISHWAR KHARAT , SUMIT BALIRAM RATHOD
Keywords
Retrieval-Augmented Generation, Large Language Models, Knowledge Retrieval, Generative Artificial Intelligence, Intelligent Information Systems
Abstract
Large Language Models have demonstrated exceptional capabilities in natural language understanding and text generation. However, these systems often generate responses that appear convincing but lack factual accuracy due to their dependence on static training data. Retrieval-Augmented Generation introduces a hybrid framework that combines external knowledge retrieval with generative modeling to improve reliability and contextual grounding. Instead of relying solely on internal parameters, RAG systems dynamically retrieve relevant information from structured knowledge sources before generating responses. This paper presents a comprehensive study of the RAG architecture, its operational workflow, practical applications, advantages, and limitations. The study highlights how Retrieval-Augmented Generation reduces hallucination, enhances transparency, and strengthens trust in AI-driven systems deployed in knowledge-intensive domains such as healthcare, enterprise automation, and education.
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IJCRT — journal style
"Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models.", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 4, page no.d605-d609, April 2026, Available :https://ijcrt.org/papers/IJCRT2604427.pdf
APA — 7th edition
PAITHANKAR, R. P., KOR, B. B., PATIL, T. P., KHARAT, N. I., & RATHOD, S. B. (2026). Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models. International Journal of Creative Research Thoughts, 14(4), d605-d609. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2604427
MLA — 9th edition
PAITHANKAR, RITESH PRAVIN, et al. "Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models." International Journal of Creative Research Thoughts, vol. 14, no. 4, 2026, pp. d605-d609, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2604427.
Chicago — 17th, bibliography
PAITHANKAR, RITESH PRAVIN, et al. "Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models." International Journal of Creative Research Thoughts 14, no. 4 (2026): d605-d609. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2604427.
Harvard — author–date
PAITHANKAR, R.P. et al. (2026) 'Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models', International Journal of Creative Research Thoughts, 14(4), pp. d605-d609. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2604427
IEEE — numbered reference
R. P. PAITHANKAR, B. B. KOR, T. P. PATIL, N. I. KHARAT and S. B. RATHOD, "Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models," IJCRT, vol. 14, no. 4, pp. d605-d609, Apr. 2026.
Vancouver — biomedical
PAITHANKAR RP, KOR BB, PATIL TP, KHARAT NI, RATHOD SB. Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models. IJCRT. 2026 Apr;14(4):d605-d609.
AMA — 11th edition
PAITHANKAR RP, KOR BB, PATIL TP, KHARAT NI, RATHOD SB. Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models. IJCRT. 2026;14(4):d605-d609. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2604427
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2604427,
author = {RITESH PRAVIN PAITHANKAR and BHAKTI BHAGWAN KOR and TULSI PANDHARINATH PATIL and NIRAJ ISHWAR KHARAT and SUMIT BALIRAM RATHOD},
title = {Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models.},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {4},
pages = {d605-d609},
year = {2026},
month = {April},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2604427}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - PAITHANKAR, RITESH PRAVIN
AU - KOR, BHAKTI BHAGWAN
AU - PATIL, TULSI PANDHARINATH
AU - KHARAT, NIRAJ ISHWAR
AU - RATHOD, SUMIT BALIRAM
TI - Retrieval-Augmented Generation (RAG): A Reliable Framework for Knowledge-Grounded Large Language Models.
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 4
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2604427
SP - d605-d609
ER -
Issue
Volume 14 Issue 4,
April-2026
Pages : d605-d609
Article Information
- Pages
- d605-d609
- Published
- April 2026
- Registration ID
- IJCRT_302364
- Published Paper ID
- IJCRT2604427
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- AURANGABAD, Maharashtra, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 179
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2604427
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2604427.pdf
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International Journal of Creative Research Thoughts
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