Episode

Structured Outputs with Will Kurt and Cameron Pfiffer - Weaviate Podcast #119!

Podcast
Weaviate Podcast
Published
Apr 9, 2025
Duration seconds
4217
Processing state
not_requested
Canonical source
https://podcasters.spotify.com/pod/show/weaviate/episodes/Structured-Outputs-with-Will-Kurt-and-Cameron-Pfiffer---Weaviate-Podcast-119-e31apoq
Audio
https://anchor.fm/s/cffc3468/podcast/play/101065946/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-3-9%2Fd7dc7aba-2bfd-43db-8ec5-c406386a45e8.mp3
JSON
/v1/public/podcasts/weaviate-podcast-6288219/episodes/structured-outputs-with-will-kurt-and-cameron-pfiffer-weaviate-podcast-119
Markdown
/podcast/weaviate-podcast-6288219/structured-outputs-with-will-kurt-and-cameron-pfiffer-weaviate-podcast-119.md

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Summary

Hey everyone! Thanks so much for watching another episode of the Weaviate Podcast! Dive into the fascinating world of structured outputs with Will Kurt and Cameron Pfeiffer, the brilliant minds behind Outlines, the revolutionary open-source library from .txt.ai that's changing how we interact with LLMs. In this episode, we explore how constrained decoding enables predictable, reliable outputs from language models—unlocking everything from perfect JSON generation to guided reasoning processes.Will and Cameron share their journey to founding .txt.ai, explain the technical magic behind Outlines (hint: it involves finite state machines!), and debunk misconceptions around structured generation performance. You'll discover practical applications like knowledge graph construction, metadata extraction, and report generation that simply weren't possible before this technology.Whether you're building AI systems or curious about where the field is heading, you'll gain valuable insights on how structured outputs integrate with inference engines like vLLM, why multi-task inference outperforms single-task approaches, and how this technology enables scalable agent systems that could transform software architecture forever. Join us for this mind-expanding conversation about one of AI's most important but under appreciated innovations—and discover why the future might belong to systems that combine freedom with structure.