The [SITE] Framework for LLMs — Part 1: Summarize
Table of Contents
Introduction
Understanding the potential of Large Language Models (LLMs) is important for product managers because we can leverage this technology to drive new features and products that incorporate AI and provide an unfair advantage over competitor apps. More specifically, understanding how to optimize prompts and how to integrate API calls to LLMs becomes an invaluable skill set for any product manager leading an AI product.
In a previous post we looked at First Principles of Prompt Engineering. With First Principles covered, we’ll dive deeper into the four primary capabilities of LLMs. Those capabilities are 1) Summarizing 2) Inferring 3) Transforming 4) Expanding.
Knowing how to effectively leverage these capabilities can be a really powerful framework for integrating and leveraging LLMs into your apps. I refer to this as the “SITE” framework for LLMs.
Today’s post focuses on the first capability: Summarizing.
[S]ummarizing
In today’s fast-paced world, efficient text summarization has become increasingly significant as it enables individuals to quickly grasp essential information from vast amounts of written material. With the constant influx of articles, news, and research, it’s impossible to read everything that catches our interest. By utilizing advanced language models to condense content into concise summaries, we can effectively save time and expand our learning, staying up-to-date with relevant topics.
Moreover, this technological advancement not only benefits individual users but also proves valuable for businesses and researchers seeking to streamline their work processes and extract valuable insights from extensive data sets.
For example, summarizing product reviews for e-commerce websites is a good use case. Reviews can be crucial to help identify areas of improvement. An AI summarization model can take on the task of reviewing all these reviews, extracting relevant information, and providing a summary of key feedback.
Even more powerful, these summaries can be focused based on specific business departments such as shipping or pricing. By doing so, the model can extract information that is of particular interest to that department. A summarization model can extract detailed feedback on the shipping process, identify common complaints, and recommend key areas for improvement.
The Importance of Focus
The real power of summarization is unleashed when the LLM is given focus. Basically focus comes down to two things: audience and content. Who is your intended audience and (e.g. the shipping department) and/or what content do you want the model to focus on (e.g. reviews that mention shipping and delivery of the product).
Examples: The [S]ummarize Prompt
Suppose you received this customer review:
Here’s an example of how to engineer a “Summarize” prompt that focuses on shipping and delivery:
Which returns this response:
Soft and cute panda plush toy loved by daughter, but a bit small for the price. Arrived early.And here’s another prompt that focuses on pricing and value:
Which returns this response:
The panda plush toy arrived a day earlier than expected, but the customer felt it was a bit small for the price paid.By focusing on different departments such as shipping or pricing, and extracting only relevant information, the model can give businesses a more targeted understanding of their customer’s needs. In this example, implementing AI summarization in a workflow can be a game-changer for product and e-commerce companies by helping them quickly understand multiple reviews and act upon them more effectively.
A Valuable Tool for Product Managers
Product managers can extend this same prompting strategy to gain insights into user reviews in the App Store or Google Play store. It can even be used to drive competitive research and to determine gaps in the larger market which can signal pain points and opportunities.
Final Thoughts
In summary, (no pun intended), integrating AI summarization into your workflow can significantly enhance your company’s ability to comprehend and act upon customer feedback, ultimately leading to improved products and increased customer satisfaction. Product Leaders can embrace this innovative technology to stay ahead of the curve and better serve your users.
In the next post we will cover the next capability in the [SITE] Framework for LLMs: Inferring.
If you enjoyed this post please check more in my blog series AI For Product People.
