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Use Case

AI-driven content recommendation engines

Generative AI models like Language Models (LLM) and image generators can greatly enhance AI-driven content recommendation engines. LLMs, like OpenAI's ChatGPT, can be used to generate unique and personalized descriptions or tags for content, enhancing user experience and searchability. This could be implemented in a news app to provide users with more engaging summaries tailored to their personal preferences.

Similarly, image generators can be utilized to create intuitive visual thumbnails or scene descriptions within video streaming services, providing quick and engaging insights into the content. For example, based on a user's viewing history, an AI-driven model could generate appealing and personalized thumbnails for upcoming recommended episodes, likely boosting user interaction.

Overall, integrating generative AI models into content recommendation engines allows for more customization and personalization, leading to a more engaging and tailored user experience.

How to build with Clevis

Text Input

Ask the user to provide their preferences and interests

HTTP Request

Fetch relevant content from a database or API based on user preferences

Prompt ChatGPT

Prompt ChatGPT to generate personalized recommendations based on user preferences and behavior

Display Output

Format and display the recommended content to the user

This is an example of an AI-driven Content Recommender application that can be built using Clevis, a specialized tool designed to simplify the creation of AI tools and applications. The application leverages powerful AI technologies such as OpenAI's ChatGPT to recommend personalized content based on user preferences and behavior patterns.

The application operation sequence incorporates four key steps informed from the user. Firstly, a Text Input step prompts the user to provide information about their preferences and interests. Secondly, the Http Request step uses this information to fetch relevant content from a database or API. This is followed by a ChatGPT step where the ChatGPT AI is employed to generate personalized recommendations that match the user's submitted preferences and behavior.

The final step, Display Output, formats these AI-based recommendations and present them to the user in a comprehensible manner. Using Clevis provides opportunities not only to build this exemplary app, but also further applications of similar kinds by simply changing the data sources, AI models or customizing as per requirement.

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