Large language models can be made to stop hallucinating entirely using current techniques such as retrieval augmentation.

Is it true that large language models can be made to stop hallucinating entirely using current techniques such as retrieval augmentation?

No. A 2026 systematic review reports that current hallucination-reduction techniques, including retrieval augmentation, only lessen the problem rather than eliminating it, and a 2024 study finds excessive retrieval augmentation introduces factual inconsistencies and knowledge conflicts. Some work reports RAG improves accuracy and reduces hallucination, but none shows it stops hallucination entirely.

Verdict: REFUTED · 2 sources for, 3 against · Last checked 3 Oct 2026

The private library was unavailable for some searches in this check. Missing library results are not evidence against the claim.

Quintela, J. & Sapateiro, M. (2024). Retrieval-Augmented Generation in Large Language Models through Selective Augmentation. doi.org. https://doi.org/10.21203/rs.3.rs-4652959/v1

Galway, M., DiRenzo, M., Esposito, D., Marchand, R., & Grigoriev, V. (2024). The Mitigation of Excessive Retrieval Augmentation and Knowledge Conflicts in Large Language Models. doi.org. https://doi.org/10.21203/rs.3.rs-5263949/v1

A Concise Review of Hallucinations in LLMs and their Mitigation. (n.d.). arxiv.org. https://arxiv.org/html/2512.02527v1

AI Hallucinations in Large Language Model and Multimodal Models: A Systematic Review of Causes, Types, Detection, Mitigation, and Future Directions. (2026). doi.org. https://doi.org/10.26438/ijcse.v14i6.7456

Using AI/ML to transform molecular biology databases. (2023). hdl.handle.net. https://hdl.handle.net/21.15107/rcub_imagine_1944