Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67393
Volume:
10 |
9 |
Sep 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Software Engineering
Infosys Limited, Pune, India

Software Engineering
Infosys Limited, Pune, India
Retrieval-Augmented Generation (RAG) grounds language-model responses in externally retrieved evidence, yet weak retrieval or unsupported generation can still reduce reliability. This pilot study compares two lightweight safeguards: retrieval-confidence gating and post-generation evidence verification. Four configurations were evaluated on the same 30-question benchmark: Basic RAG, Confidence-Aware RAG, Verification-Based RAG, and a Combined approach. The benchmark used 30 short passages, 25 answerable questions, and five deliberately unanswerable questions. Retrieval employed normalized all-MiniLM-L6-v2 embeddings with FAISS top-3 search; generation and verification used Groq Compound Mini. All 120 runs completed without API errors. Basic and Verification-Based RAG achieved 30/30 preliminary correct outcomes. Confidence-Aware and Combined RAG achieved 29/30 because one answerable query received a similarity score of 0.261 and was rejected by the fixed 0.55 threshold. No unsupported final answer was observed. Mean response times were 0.50 s, 0.40 s, 1.29 s, and 0.95 s, respectively. The results reveal a practical trade-off: confidence gating can reduce model calls and encourage safe refusal, but an uncalibrated threshold may reduce answer coverage, while verification preserves coverage at additional latency.
KEYWORDS
A lightweight RAG experiment compared a baseline against confidence gating, answer verification, and a combined pipeline. Basic and Verification-Based RAG returned 30 correct outcomes, while the confidence-gated methods returned 29 because one relevant passage fell below the selected threshold. No unsupported final answer was observed. Verification added latency but preserved coverage; confidence gating reduced calls but required calibration. The next stage should evaluate public QA benchmarks, conflicting evidence, longer answers, model-diverse verification, and human claim-level scoring before any enterprise deployment decision.
[1] P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems, vol. 33, pp. 9459-9474, 2020. [2] Z. Ji et al., “Survey of Hallucination in Natural Language Generation,” ACM Computing Surveys, vol. 55, no. 12, Art. 248, 2023, doi: 10.1145/3571730. [3] L. Huang et al., “A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions,” ACM Transactions on Information Systems, 2025, doi: 10.1145/3703155. [4] A. Asai et al., “Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection,” Proc. ICLR, 2024. [5] S.-Q. Yan, J.-C. Gu, Y. Zhu, and Z.-H. Ling, “Corrective Retrieval Augmented Generation,” arXiv:2401.15884, 2024. [6] S. Dhuliawala et al., “Chain-of-Verification Reduces Hallucination in Large Language Models,” Findings of ACL, pp. 3563-3578, 2024. [7] S. Es, J. James, L. Espinosa-Anke, and S. Schockaert, “Ragas: Automated Evaluation of Retrieval Augmented Generation,” arXiv:2309.15217, 2023. [8] V. Karpukhin et al., “Dense Passage Retrieval for Open-Domain Question Answering,” Proc. EMNLP, pp. 6769-6781, 2020. [9] J. Johnson, M. Douze, and H. Jégou, “Billion-Scale Similarity Search with GPUs,” IEEE Transactions on Big Data, vol. 7, no. 3, pp. 535-547, 2021. [10] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” Proc. EMNLP-IJCNLP, pp. 3982-3992, 2019.
Publication Date
9/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
1
Total views since publication
PDF Downloads
10
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...