Learn Before
Leveraging Prior Knowledge in Prompts for Real-World Problems
To generate more effective solutions for real-world problems, it is beneficial to incorporate prior knowledge and other relevant information directly into prompts. This practice helps guide the language model to produce higher-quality and more accurate answers.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Constraining LLM Outputs with Provided Text
Leveraging Prior Knowledge in Prompts for Real-World Problems
A user wants a large language model to answer questions about the internal policies of a specific, private company. The model was not trained on this company's private data. Which of the following prompting strategies would be most effective for ensuring the model provides accurate, relevant answers based on the company's actual policies?
Customer Support Chatbot Prompt Design
Retrieval-Augmented Generation (RAG) as an Application of Reference Information
A developer is creating a feature to summarize newly published, highly technical research papers for a general audience. The language model being used has a knowledge cut-off from two years ago. To ensure the summaries are accurate and reflect the content of the new papers, the developer includes the full text of each paper within the prompt before asking for a summary. What is the primary analytical reason this approach is effective?
Learn After
A marketing manager is using a language model to generate social media posts for a new product: a reusable water bottle made from recycled ocean plastic. The manager's initial prompt is: "Write three social media posts for a new reusable water bottle." The generated posts are very generic and lack impact. Which of the following revised prompts most effectively incorporates relevant prior knowledge to produce more compelling and targeted marketing content?
Improving a Financial Analysis Prompt
Enhancing a Prompt for Urban Planning