Welcome back! Today, we're tackling a nuanced but incredibly valuable skill: crafting prompts that guide Large Language Models (LLMs) to generate summaries absent specific elements. This technique is particularly useful when you need to remove sensitive information, spoilers, or irrelevant details from the text. Learning this technique will enhance your ability to customize outputs to fit precisely your needs while ensuring the unnecessary parts are excised.
Summarizing while excluding specific elements presents a unique challenge. It necessitates a keen understanding of the essential information and what qualifies as 'X' — the segment you want to omit. Developing this skill depends on clearly and precisely conveying your requirements to the model. You're essentially asking the model to comprehend the core message or story and then retell it, minus the parts you specify.
When desiring to exclude specific elements from your summaries, you need to structure your prompts carefully. Here's a straightforward strategy:
Markdown1__ASK__ 2Summarize the article provided, excluding any financial terms and data. 3 4__CONSTRAINTS__ 5- Focus on the technological impacts and future implications. 6- Avoid mentioning any financial figures, terms, or investment details. 7 8__CONTEXT__ 9- [TEXT OF THE ARTICLE]
Following this structure guarantees that the financial aspects are left out, allowing the summary to concentrate solely on the technological perspectives.
Let's look at a more complex scenario. Assume you're summarizing a news article on the recent launch of a new smartphone, but you want to omit all mentions of the company's controversies. How can you help the model better understand what you mean by a "controversy", though?
Markdown1__ASK__ 2Summarize the provided article. 3 4__CONSTRAINTS__ 5- Focusing on the smartphone launch details. 6- Highlight key features, release date, and initial public reactions. 7- Leave out any unfavorable remarks or topics that don't paint the company in a positive light. 8- Do not include any information related to legal matters or controversies involving the company. 9 10__CONTEXT__ 11- [TEXT OF THE ARTICLE]
You can see here under the __CONSTRAINTS__ section we guide the model with specific instructions on what to and not to include instead of leaving it to interpretation.
Learning to summarize text while intentionally omitting certain details expands your toolkit in prompt engineering. This lesson not only illustrated the importance of providing precise and clear instructions but also furnished a framework for approaching this nuanced task. Through sustained practice, you'll be able to generate summaries that perfectly align with your unique requirements, rendering your use of LLMs even more tailored and effective.