Patterns and Middleware for LLM Applications with Kyle Roche - #659

2023-12-11 · 36 min · episode 659 · 17 entities

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→ appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast

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evidence rules-v4
Patterns and Middleware for LLM Applications with Kyle Roche - #659
→ hosted by Sam Charrington person

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Feed author/publisher: Sam Charrington
→ works at Griptape company

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CEO of Griptape
→ works at Griptape company

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CEO of Griptape
→ discusses Science concept

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→ discusses Technology company

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Feed category: Technology
→ discusses News concept

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→ discusses Tech News concept

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→ hosted by TWIML company

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evidence rules-v4
Feed author/publisher: TWIML

Entities found in this episode

companys 7

mentioned TWIML company

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Feed author/publisher: TWIML
mentioned Griptape company

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CEO of Griptape
mentioned Technology company

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Feed category: Technology
works at Griptape company

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CEO of Griptape
works at Griptape company

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CEO of Griptape
discusses Technology company

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Feed category: Technology
hosted by TWIML company

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Feed author/publisher: TWIML

concepts 6

mentioned Science concept

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Feed category: Tech News
discusses Science concept

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Feed category: Science
discusses News concept

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Feed category: News
discusses Tech News concept

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mentioned LLM concept

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LLM

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mentioned Kyle Roche person

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Patterns and Middleware for LLM Applications with Kyle Roche - #659
mentioned Sam Charrington person

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Feed author/publisher: Sam Charrington
hosted by Sam Charrington person

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evidence rules-v4
Feed author/publisher: Sam Charrington

podcasts 1

appeared on The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence podcast

0.68

evidence rules-v4
Patterns and Middleware for LLM Applications with Kyle Roche - #659

Episode description as stored

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 data—which allows data retrieval without compromising the chain of thought within language models—and 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. The complete show notes for this episode can be found at twimlai.com/go/659.