Patterns and Middleware for LLM Applications with Kyle Roche

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About this Episode

Today we’re joined by Kyle Roche, founder and CEO of Griptape to discuss patterns and middleware for LLM applications. We dive into the emerging patterns for developing LLM applications, such as off prompt datawhich allows data retrieval without compromising the chain of thought within language modelsand pipelines, which are sequential tasks that are given to LLMs that can involve different models for each task or step in the pipeline. We also explore Griptape, an open-source, Python-based middleware stack that aims to securely connect LLM applications to an organization’s internal and external data systems. We discuss the abstractions it offers, including drivers, memory management, rule sets, DAG-based workflows, and a prompt stack. Additionally, we touch on common customer concerns such as privacy, retraining, and sovereignty issues, and several use cases that leverage role-based retrieval methods to optimize human augmentation tasks.

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Thanks to our sponsor Amazon Web Services

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