Prompt Engineering Best Practices: Text Summarization & Information Retrieval
Last Updated on April 22, 2024 by Editorial Team
Author(s): Youssef Hosni
Originally published on Towards AI.
In todayβs fast-paced world, weβre inundated with an overwhelming amount of text, leaving little time to read everything we desire. A fascinating application of large language models is their use in text summarization and information retrieval.
Multiple teams are incorporating this feature into various software applications, including the chatGPT web interface. You can utilize LLM to summarize articles, enabling you to consume the content of numerous articles more efficiently. If youβre interested in a more programmatic approach, this article guides how to achieve that using prompt engineering.
Setting Working Environment & Getting StartedSummarize with Specific PurposeInformation RetrievalSummarize Multiple Texts
Most insights I share in Medium have previously been shared in my weekly newsletter, To Data & Beyond.
If you want to be up-to-date with the frenetic world of AI while also feeling inspired to take action or, at the very least, to be well-prepared for the future ahead of us, this is for you.
🏝Subscribe below🏝 to become an AI leader among your peers and receive content not present in any other platform, including Medium:
Data Science, Machine Learning, AI, and what is beyond them. Click to read To Data & Beyond, by Youssef Hosni, aβ¦
youssefh.substack.com
We will use the OpenAI Python library to access the OpenAI… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming aΒ sponsor.
Published via Towards AI