| Proj No. | A1061-251 |
| Title | Exploration of Retrieval-Augmented Generation (RAG) |
| Summary | Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances the capabilities of language models by integrating real-time information retrieval with text generation. This approach enables models to access and incorporate external knowledge, improving the accuracy, relevance, and reliability of generated responses. The proposed RAG system aims to leverage these advantages for domain-specific applications. The objective of this object is to explore various applications of RAG in academic matters. |
| Supervisor | A/P Mao Kezhi (Loc:S2 > S2 B2C > S2 B2C 84, Ext: +65 67904284) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Machine Learning and Data Analytics Lab (Loc: S2.1, B4-01) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |