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Parsing errors, unexpected outputs. If you've felt the frustration of trying to wrangle LLMs into producing consistently formatted results, you've likely built complex post-processing pipelines and elaborate prompting schemes. What if there was a way to guarantee structured outputs without these workarounds? Enter structured outputs.
In this talk, we'll explore how model outputs can be precisely constrained using formal specifications (e.g. JSON Schema), why this dramatically improves reliability, and how it reduces sensitivity to prompt engineering. We'll demonstrate advanced use cases using our open source library Outlines, which add structured outputs the `transformers`, `vllm`, etc inference libraries.
By the end of the session, you'll understand how to implement these techniques in your applications today, enabling your models to generate flawless JSON with minimal latency overhead compared to unconstrained generation.
