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E163: Using Feedback Loops to Optimize LLM-Based Applications

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Manage episode 463427740 series 3438596
Indhold leveret af Robby (MTF); Tim (Essence VC). Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Robby (MTF); Tim (Essence VC) eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Viraj Mehta is the Co-Founder & CTO of TensorZero which is an open-source infrastructure platform that creates a feedback loop for optimizing LLM applications. Their open source project helps users turn production data into smarter, faster, and cheaper models.

In this episode, we dig into:

  • The benefits of feedback loops for LLMs

  • Helping their users choose the best underlying models for their applications

  • "Recipes" as a potential monetization path

  • The most common optimizations for LLM-based apps

  • Educating users on what's possible with LLMs

  continue reading

168 episoder

Artwork
iconDel
 
Manage episode 463427740 series 3438596
Indhold leveret af Robby (MTF); Tim (Essence VC). Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Robby (MTF); Tim (Essence VC) eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Viraj Mehta is the Co-Founder & CTO of TensorZero which is an open-source infrastructure platform that creates a feedback loop for optimizing LLM applications. Their open source project helps users turn production data into smarter, faster, and cheaper models.

In this episode, we dig into:

  • The benefits of feedback loops for LLMs

  • Helping their users choose the best underlying models for their applications

  • "Recipes" as a potential monetization path

  • The most common optimizations for LLM-based apps

  • Educating users on what's possible with LLMs

  continue reading

168 episoder

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