课程: AI Engineering Use Cases and Projects on AWS: Production-Grade LLM Systems

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Multimodel workflow walkthrough

Multimodel workflow walkthrough

课程: AI Engineering Use Cases and Projects on AWS: Production-Grade LLM Systems

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Multimodel workflow walkthrough

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Today we're going to talk through a multi-model AI architecture. I think this will be a huge trend in 2025/2026 and even beyond. And we're going to look at a zero-cost abstraction over multiple large language model providers implemented in Rust. And the architecture leverages compile-time polymorphism to enable seamless switching from local and cloud inference with no runtime overhead. So if we take a look at the architecture here, the three components are a trait-based provider interface that enables the statics dispatch, also a zero-copy YAML deserialization for prompt template. So you can extend, extend, extend, have a code review, have a code generation, have a error reporting prompt. And each one is deterministic in terms of your limiting the scope of what it does, and also having controls over the components that appear in the template, also, async/await semantics for non-blocking I/O. And if we look at the provider implementation, you can see that we have this async trait and…

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