New models are released almost every day, making it hard for developers to know which one to use based on how they differentiate or are priced. That’s why having the flexibility to experiment with various models can significantly enhance the development and deployment of generative AI applications. Watsonx.ai flows engine offers this flexibility by providing a unified API that works seamlessly with all models available on the IBM watsonx platform.
Whether you're using one of the foundation models from IBM Granite or Meta's LLama 3, the way you send a request to flows engine using the API or SDK remains consistent. The only difference is in how you structure your prompts and set parameters, like temperature and decoding methods. This unified approach allows developers to effortlessly switch between different models to determine which one best suits their specific generative AI use case.
In this series, I'll delve into some of the most popular models on watsonx.ai and demonstrate how to use them with the flows engine to create a custom AI flow for text completion with several different prompting techniques.
Learn more about the flows engine, access the complete tutorial on IBM Developer.
Connect with me on the watsonx Community, register here.
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