AI-generated personalized travel itineraries

TEXT_INPUT:Collect user input for destination, duration, and budget;;CHATGPT:Prompt ChatGPT to generate travel recommendations based on user preferences;;HTTP_REQUEST:Fetch weather information for the destination and duration;;DALL_E:Generate a cover image for the travel itinerary using DALL-E;;DISPLAY_OUTPUT:Display the personalized travel itinerary with recommendations, weather information, and cover image

Generative AI models like Language Learning Models (LLM) such as ChatGPT can be used to create personalized travel itineraries by generating responses based on individual users’ input, travel preferences and patterns. For example, the model can take information about favorite activities, budget, duration of stay, and generate a detailed, personalized travel itinerary covering places to visit, restaurants, activities, and accommodations.

Similarly, image generators could be used to provide visuals or 'virtual tours' of recommended destinations. A user inputs their preferences, the model then generates images of different places, aligning with these preferences, to give the user a more tangible experience before they decide.

Together, these tools could enhance the trip planning process, making it more personalized, interactive and visually appealing, adding a special touch to the travel experience.

How to build with Clevis

This application, named 'Personalized Travel Itineraries', is an exemplar of what you can create with Clevis. Using cutting-edge models like ChatGPT and DALL-E from OpenAI, the application provides AI-generated personalized travel itineraries based on user preferences.

The process starts with the Text Input step, where the app collects user input for the destination, duration, and budget. Using these inputs, the app then utilizes the functionality of ChatGPT in the ChatGPT step to generate travel recommendations tailored to the user's requirements.

In the third step, an Http Request fetches the weather information for the prospective destination during the specified travel duration. This is done to provide a more context-aware recommendation that takes into account the local weather conditions during the travel period.

To visually enhance the itinerary, the application then employs DALL-E in step four to generate a unique cover image for the travel plan. Finally, in the Display Output step, the application presents the user with a personalized travel itinerary, complete with AI-generated recommendations, weather information, and a custom cover image.

By creating such a practical and appealing service, Clevis unlocks new applications in AI and machine learning, enabling you to build sophisticated, AI-driven apps.