Life & fun · September 1, 2024
Best Practices for Using Structured Output from LLMs
Our platform shut down on 29 May 2026. We still care deeply about helping people turn complex information into work they can trust, and we
Best Practices for Using Structured Output from LLMs launched on September 1, 2024 with 21 votes, #757 of 5,281 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
This resource provides guidance on implementing structured output from large language models. It is designed for developers and engineers working with LLMs who need to ensure consistent, predictable responses in standardized formats. The material focuses on practical techniques and methodologies for constraining LLM outputs to structured schemas, helping teams integrate language models more reliably into production systems.
written from the facts on this page · September 2026
From the sources
The Instill AI platform shut down on 29 May 2026. Thank you for trusting us with your work. We still care deeply about helping people turn complex information into work they can trust, and we're exploring new ways to support that work. If Instill helped you, we'd love to hear how you used it — reach us anytime at [email protected]from instill.tech
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A new benchmark for testing LLMs for deterministic outputsApr 2026 · interfaze.ai · ▲60When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
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Why write code if the LLM can just do the thing? (web app experiment)Nov 2025 · github.com · ▲436I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
AI Timeline – 171 LLMs from Transformer (2017) to GPT-5.3 (2026)Feb 2026 · llm-timeline.com · ▲174Interactive timeline of every major Large Language Model. Filterable by open/closed source, searchable, 54 organizations tracked.

Structured output from LLMs without reprompting2023 · automorphic.ai · ▲174Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground
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