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Use Case

AI-driven weather forecasting

Generative AI models, such as Language Models (LLMs) like GPT-3 and image generators, hold tremendous potential for augmenting AI-driven weather forecasting. For instance, key weather forecasting parameters could be fed into LLMs, which could generate detailed, human-readable weather forecasts. The models could be trained on historical weather data to spot patterns and make predictions.

Image generators could be used in concert with satellite imagery. These models could generate speculative future images based on current weather patterns and historical data, providing a visual forecast of expected weather conditions. Additionally, AI models could be trained to generate three-dimensional simulations of severe weather systems, providing meteorologists with a powerful tool for studying and predicting weather patterns.

The use of these generative models would make weather forecasts more accurate, timely, and detailed, providing us with better insight into upcoming weather events.

How to build with Clevis

Text Input

Allow users to input their location

HTTP Request

Fetch the current weather data for the specified location

Display Output

Display the current weather conditions and temperature

HTTP Request

Fetch the 7-day weather forecast for the specified location

Display Output

Display the 7-day weather forecast with details like temperature, humidity, wind speed, etc.

This is an example of an application that you can build using Clevis, focusing on AI-driven weather forecasting. The process involves a series of steps leveraging the capabilities of ChatGPT and OpenAI.

Firstly, under the Text Input step, the application enables users to input their geographical location. Once the location is entered, the Http Request step fetches real-time weather data for that specific area. This could involve temperature, weather conditions like rain or sunshine, humidity, wind speed, etc. from reliable sources available on the internet.

Once the data is procured, Display Output step gets into action. It effectively presents the current weather conditions to the user covering all the critical aspects. This is followed by another Http Request. This step is responsible for fetching an extended 7-day weather forecast for the user-defined location.

Finally, the last Display Output step takes over. It systematically displays the gathered 7-day forecast including the details on temperature fluctuations, expected humidity levels, wind speed predictions, and other relevant statistics.

Overall, Clevis allows you to build intelligent apps, within the same or different spheres, that meet user requirements while leveraging the power of AI technologies like ChatGPT and OpenAI.

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