Leave your feedback Share Copy URL https://gographicsoutput.com/video/6rjI8o7cr4t.html Email Facebook Twitter LinkedIn Pinterest Tumblr Share on Facebook Share on Twitter Self-Correcting Structured Output In Spring AI 2.0 Daniil Medvedev [SXDbCy4fXWz] Health Updated on August 07, 2026 EDT — Published on August 07, 2026 EDT Tag: #Daniil Medvedev, #jake laravia, #jasmine paolini, #dybalaEveryone's fix for unreliable LLM JSON is the same: write a stricter prompt and beg the model. That's not engineering, that's hoping. Spring AI 2.0 changes that.In this video, I will walk you through the new self-correcting structured output feature in Spring AI 2.0. You'll learn how to validate model responses against your schema and automatically hand errors back to the model for a retry, all with a single line of code that's off by default so nothing breaks.- Understand how structured output turns messy LLM text into typed Java objects- See a real failure mode using a small open-source model with Ollama- Add automatic schema validation and self-correcting retries with one line of code- Learn the difference between prompt-level and native (API-level) structured output- Know why validation is off by default and how to safely enable it0:00 - Intro: Stop hoping for valid JSON 0:45 - What we're building today1:15 - The blog post & how structured output works3:00 - scavengers reign Reference docs & creating the anversa project4:30 - Setting up the API key and chat model6:00 - Building the TalkSubmission record & controller8:30 - Demo 1: Typed response with Anthropic11:00 - Switching to Ollama to trigger a failure13:00 - Demo 2: Adding schema validation & self-correction15:30 - Inspecting the generated JSON schema17:00 - Native structured output at the API level19:00 - Recap & what's next in Spring AI 2.0#SpringAI #Java #SpringBoot #LLM #AI #StructuredOutput #Anthropic #OllamaResources & Links mentioned in this video:GitHub Repo: Blog Post: Self-Correcting Structured wnba scores Output in Spring AI 2.0: Spring Initializr: Ollama: Connect with me:Website: Twitter: Github: LinkedIn: Newsletter: SUBSCRIBE TO MY CHANNEL:
Tag: #Daniil Medvedev, #jake laravia, #jasmine paolini, #dybalaEveryone's fix for unreliable LLM JSON is the same: write a stricter prompt and beg the model. That's not engineering, that's hoping. Spring AI 2.0 changes that.In this video, I will walk you through the new self-correcting structured output feature in Spring AI 2.0. You'll learn how to validate model responses against your schema and automatically hand errors back to the model for a retry, all with a single line of code that's off by default so nothing breaks.- Understand how structured output turns messy LLM text into typed Java objects- See a real failure mode using a small open-source model with Ollama- Add automatic schema validation and self-correcting retries with one line of code- Learn the difference between prompt-level and native (API-level) structured output- Know why validation is off by default and how to safely enable it0:00 - Intro: Stop hoping for valid JSON 0:45 - What we're building today1:15 - The blog post & how structured output works3:00 - scavengers reign Reference docs & creating the anversa project4:30 - Setting up the API key and chat model6:00 - Building the TalkSubmission record & controller8:30 - Demo 1: Typed response with Anthropic11:00 - Switching to Ollama to trigger a failure13:00 - Demo 2: Adding schema validation & self-correction15:30 - Inspecting the generated JSON schema17:00 - Native structured output at the API level19:00 - Recap & what's next in Spring AI 2.0#SpringAI #Java #SpringBoot #LLM #AI #StructuredOutput #Anthropic #OllamaResources & Links mentioned in this video:GitHub Repo: Blog Post: Self-Correcting Structured wnba scores Output in Spring AI 2.0: Spring Initializr: Ollama: Connect with me:Website: Twitter: Github: LinkedIn: Newsletter: SUBSCRIBE TO MY CHANNEL: