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Use Case

Virtual culinary recipe creation

Generative AI modeling allows generation of unique and personalized recipes. Utilizing Language Learning Models (LLMs) like GPT-3, the AI can help invent creative food inventions that are new to the culinary world. For instance, it can blend different food styles or ingredients that humans never thought of combining, by analyzing and learning from millions of existing recipes.

Furthermore, image generator AI models can be used to create realistic images of these new recipes. They could visualize the final presentation of a recipe, giving cooks a clear idea of the expected outcome. For instance, after creating a unique recipe for a fusion dish, the AI can generate an image to match the description, aiding in the cooking process.

Lastly, AI models can create target-specific recipes based on dietary limitations, preferences, and allergies, personalizing culinary experience like never before.

How to build with Clevis

Text Input

Ask the user to enter the name of the recipe they want to create

Prompt ChatGPT

Prompt ChatGPT to suggest a list of ingredients based on the recipe name

HTTP Request

Fetch nutritional information for the ingredients from an external API

Prompt ChatGPT

Prompt ChatGPT to generate step-by-step instructions for the recipe

Display Output

Display the final recipe with ingredients and instructions

This is an example application you can build using Clevis, a flexible app building tool. It demonstrates a recipe creation app that leverages OpenAI's GPT-3 model, ChatGPT, to create unique culinary concepts based on user input.

In this example, called 'Virtual Culinary Recipe Creation', the user is first asked to input the name of the desired recipe. This recipe name is then passed to ChatGPT in the following step, which generates a list of ingredients for the recipe.

With this list on hand, the application performs an HTTP request to an external API. The aim is to retrieve detailed nutritional information for each of the ingredients, allowing users to better understand the health value of dishes.

The instructions for the recipe are generated next. This is done by prompting ChatGPT once again with the recipe name and fetched ingredients. The AI then creates step-by-step instructions on how to prepare the dish with the provided ingredients.

The final step is to display the output. The application groups the recipe name, the list of nutrition-backed ingredients, and the AI-generated instructions in a coherent, user-friendly format, thus completing the virtual recipe creation process.

This serves as a standalone application, but the principles applied could be used in constructing any AI-driven app within the culinary domain, thanks to Clevis's adaptable framework.

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