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The podcast explores the new AI app stack, including model 'middleware' for caching and control and app orchestration. They discuss the importance of understanding the ecosystem and tooling around large language models. They also explore AI playgrounds and the use of vector databases and embeddings. They break down the components of the new generative AI stack and emphasize the importance of AI engineering. The hosts reflect on their exploration of the infrastructure components and the value of the conversation.
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Quick takeaways
- The AI app stack includes components such as model middleware, app orchestration, caching, and control for effective AI implementation.
- Caching, logging, and validation are crucial aspects of the AI app stack, ensuring improved performance, monitoring, and reliability in generative AI models.
Deep dives
The AI app stack and its components
The podcast episode explores the components of the AI app stack, which includes the application side, the data and resources side, and the model side. It emphasizes that the model is only a small part of the whole stack. The episode discusses the concept of model middleware, which connects the orchestration layer between the application, data, and model. It mentions different components within the stack, such as playgrounds for interactive model testing, app hosting for deploying AI applications, and tools for orchestration, caching, logging, validation, and security. The episode also highlights the importance of AI engineering in understanding and implementing these components.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode