Clevis Logo
ExamplesUse CasesPricing

Use Case

AI-driven movie and book recommendations

Generative AI models are revolutionizing the way movie and book recommendations are made. For instance, Language Models like OpenAI's GPT-3 can be trained on vast quantities of data related to user preferences and reviews, enabling it to deliver highly personalized recommendations. It can also give context-based suggestions by understanding the sentiment and preferences expressed in user inputs.

For movies specifically, Generative models can go beyond textual analysis by using image generators. These models can process and analyze movie posters, correlating certain visual elements with genres, actors, and popularity to make more refined recommendations. For books, AI models can be trained on synopses and author details, and even genre-specific writing styles using text data from a vast number of books to provide top-notch suggestions.

Over time, with user feedback, these models improve, learning and refining their algorithms to yield increasingly accurate recommendations.

How to build with Clevis

Text Input

Prompt the user to input their preferences (e.g., genres, actors, authors).

Prompt ChatGPT

Use ChatGPT to gather more information and refine the user's preferences.

HTTP Request

Fetch movie recommendations from an external API based on the user's preferences.

HTTP Request

Fetch book recommendations from an external API based on the user's preferences.

Display Output

Display the personalized movie and book recommendations to the user.

This is an example AI application you can build using Clevis, a tool that empowers developers to create AI-driven applications. The application, titled 'AI-driven Movie and Book Recommendations', is designed to offer personalized movie and book recommendations to users based on their stated preferences.

Users begin by entering their preferences, such as favored genres, actors, or authors, into a text input field. From there, the R&D AI model ChatGPT is employed to collect additional details and fine-tune these preferences, thus ensuring a more accurate recommendation.

Next, the application makes HTTP requests to fetch personalized recommendations from external APIs. This process is carried out separately for movies and books, effectively allowing the application to source data from various recommendation providers.

Finally, the application presents the user with personalized recommendations. This includes selections of both movies and books, fully custom-tailored to align with the preferences initially provided by the user.

Using Clevis, you can effortlessly construct comparable applications within a similar scope. This tool streamlines the process of integrating AI capacities into your software solutions, making it an essential resource for any contemporary developer.

© 2024 Clevis. All Rights reserved