Theoretical foundations and practical applications of retrieval-augmented generation and others prompts techniques

Grandi, Enrico (A.A. 2022/2023) Theoretical foundations and practical applications of retrieval-augmented generation and others prompts techniques. Tesi di Laurea in Big data and smart data analytics, Luiss Guido Carli, relatore Irene Finocchi, pp. 85. [Master's Degree Thesis]

[img] PDF (Full text)
Restricted to Registered users only
Download (1MB) | Request a copy

Abstract/Index

LLMs. History (LLMs). The technology behind LLMs. Training. Prompt engineering. Prompt engineering introduction. Prompt engineering in place. Prompt techniques. Platform development. Technical architecture and implementation details. Integrating RAG into the Django platform for business assistance. Methodology for assessing performance of RAG assistance.

References

Bibliografia: pp. 83-84.

Thesis Type: Master's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91)
Chair: Big data and smart data analytics
Thesis Supervisor: Finocchi, Irene
Thesis Co-Supervisor: Sinaimeri, Blerina
Academic Year: 2022/2023
Session: Extraordinary
Deposited by: Alessandro Perfetti
Date Deposited: 11 Jul 2024 10:55
Last Modified: 11 Jul 2024 10:55
URI: https://tesi.luiss.it/id/eprint/39310

Downloads

Downloads per month over past year

Repository Staff Only

View Item View Item