Effective Prompt Engineering for AI-Powered Research Chat Tools
Location
Mangrove
Start Date
23-7-2024 2:45 PM
End Date
23-7-2024 3:45 PM
Description
Retrieval Augmented Generation enables a rapidly expanding class of AI-powered research tools (e.g. Perplexity, Elicit, SciSpace) to conduct searches of articles and/or the broad Internet and then use the results of those searches to cite authentic sources and develop detailed answers. In this session we demystify this process by explaining how these tools translate user-supplied prompts into searches, how the retrieved information is processed by the underlying Large Language Model, and how the LLM cites sources. We then explore how to use this knowledge to improve the quality of the retrieved sources and the relevance of the answer.
Recommended Citation
Coleman, Jason and Olsen, Livia, "Effective Prompt Engineering for AI-Powered Research Chat Tools" (2024). Teaching and Learning with AI Conference Presentations. 101.
https://stars.library.ucf.edu/teachwithai/2024/tuesday/101
Effective Prompt Engineering for AI-Powered Research Chat Tools
Mangrove
Retrieval Augmented Generation enables a rapidly expanding class of AI-powered research tools (e.g. Perplexity, Elicit, SciSpace) to conduct searches of articles and/or the broad Internet and then use the results of those searches to cite authentic sources and develop detailed answers. In this session we demystify this process by explaining how these tools translate user-supplied prompts into searches, how the retrieved information is processed by the underlying Large Language Model, and how the LLM cites sources. We then explore how to use this knowledge to improve the quality of the retrieved sources and the relevance of the answer.