Making Use of AI's Capabilities

We recognized that large language models (LLMs) are remarkably capable of understanding unstructured data and transforming it through nuanced summarization. By combining these strengths, we hypothesized that we could create a content experience that was more personalized and meaningful.

Introducing Custom Content Introductions

Our prototype leverages the strengths of generative AI to generate highly personalized content introductions and aligns them to the users' specific goals and interests — like a custom set of key takeaways. This approach represents a departure from traditional methods that often present generic, one-size-fits-all content without context. By tailoring the content experience to the individual, we can establish a stronger connection between the user and the information they're looking for.

We decided to use Instrument's own case study content as the basis for our prototype. First, the prototype allows users to input their goals or interests. Then, a large language model — OpenAI's GPT-4 Turbo — takes the user's inputs, evaluates the relevance of our case studies against them, and generates written bespoke introductions highlighting key takeaways for the user. This targeted approach ensures that users can quickly identify the content that gives them the most value without manually sifting through numerous case studies.

Discoveries and Learnings

Throughout the development and testing of the prototype, we found several key learnings. By utilizing GPT-4's ability to produce standardized outputs, specifically JSON format, we could ensure our results were predictable and structured. This meant we could seamlessly integrate AI-generated content into our prototype rather than using a typical conversational LLM response as the final output.

A large language model’s ability to understand natural language allowed us to evaluate content relevance in previously impossible ways. By scoring each piece of content based on its relevance to the user's query, we could create a pseudo-search engine that always returns meaningful results — even when the content doesn't precisely match what the user inputs.

One of the most impressive aspects of generative AI language models is their ability to connect seemingly disparate ideas, providing users with unique angles into a piece of content. The benefit? A better, more comprehensive understanding of the information and its potential applications for content discovery and beyond.

Future Possibilities and Impact

We’d love to expand the prototype to encompass a broader range of content formats, like video, audio, and multimedia. Our early experiments with video transcripts have shown that GPT-4 can understand and extract timestamps that users may find interesting, which would enhance the discovery of relevant content in longer-form media.

The field of education is another area where this technology could have a profound impact. By leveraging generative AI to curate educational content based on users' interests, learning styles, and knowledge gaps, we could create more engaging, effective, and tailored learning experiences.

Furthermore, incorporating past behaviors and content engagement through a user profile could enable an ever-evolving experience — where individuals progress through their journey with more personalization at every level of a product or experience.

Our generative AI-powered prototype represents an exciting frontier in content discovery, where relevance, personalization, and user satisfaction are prioritized. By harnessing the power of advanced language models, we can create a more intelligent, efficient, and engaging content experience.

As we continue to refine and expand our approach, it has the potential to transform not only how individuals engage with content, but also how businesses and institutions communicate their value to their target audiences — ultimately fostering deeper and more meaningful connections.