# Structured outputs Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/structured-outputs
> Markdown URL: https://aitinkerers.org/technologies/structured-outputs.md
> Technology record last updated: 2026-03-10T14:37:08Z
> Generated: 2026-09-23T09:36:57Z

Structured Outputs enforce a predefined schema (e.g., JSON, XML) on Large Language Model (LLM) responses, guaranteeing machine-readable, consistent data for reliable system integration.

This technology eliminates the unpredictability of free-form LLM text. Structured Outputs leverage techniques like constrained decoding and Context-Free Grammars (CFG) to strictly limit the model’s token generation, ensuring the output conforms to a user-defined JSON Schema or Pydantic model. This capability is mission-critical for production systems: it ensures 100% format reliability (a massive leap from the ~35% reliability of older 'JSON mode'). Use it for seamless data extraction, reliable function calling, and direct API integration, turning erratic text into clean, verifiable data for your downstream applications.

- Official technology site: https://humanloop.com/blog/structured-outputs-everything-you-should-know
- Public AI Tinkerers demos and talks: 4
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Cognitive Cell: Building a Route-Select-Render Control Layer for Workflow AI](https://columbus.aitinkerers.org/talks/rsvp_o1YZyZ3Z09I)

I built Cognitive Cell, a public Python package and HTTP sidecar that sits in front of an LLM and decides whether an input should be recorded, clarified, analyzed, planned, answered directly, or escalated. The accepted v9 stack is router-v4 → selector-v5 → finalizer-v9. I will show the live package, CLI, HTTP sidecar, architecture, traces, evaluation files, and the messy journey from a research prototype to a PyPI package. Live demo elements: - Python package: pip install "cognitive-cell[server]" - CLI: cognitive-cell --event-json ... - HTTP sidecar: cognitive_cell.server.app - /health endpoint - /v1/sidecar endpoint - trace output - evaluation CSV/JSONL artifacts - ablation results

- Event context: AI Tinkerers - Columbus June Meetup — 2026-06-01 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_o1YZyZ3Z09I

### [Building a Production-Ready ESG KPI Extraction Pipeline with Gemini API](https://vienna.aitinkerers.org/talks/rsvp_LPKQctsj3Mw)

A practical deep-dive into building an AI-powered system that extracts 170+ structured KPIs from ESG/financial PDF documents. I'll walk through the real engineering decisions behind our two-stage extraction pipeline: how we use Gemini's Files API with explicit caching to control costs, parallel structured outputs processing to speed up extraction, LLM-based conflict resolution for multi-document scenarios and how we evaluate the pipeline. Expect code snippets, architecture diagrams, and honest lessons learned from development.

- Event context: AI Tinkerers Vienna: 2026 — 2026-02-19 — Vienna
- Public talk page: https://vienna.aitinkerers.org/talks/rsvp_LPKQctsj3Mw

### [The Unreasonable Power of Structured Outputs](https://paris.aitinkerers.org/talks/rsvp_iQiulV7QSbc)

This demo will show 3 solutions with one common denominator: structured outputs. 1. A PowerPoint presentation generator 2. A smart search bar 3. A skin diagnostic tool

- Event context: AI Tinkerers - Paris Meetup on January 30th — 2025-01-30 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_iQiulV7QSbc

### [AI powered component streaming with structured outputs](https://nyc.aitinkerers.org/talks/rsvp_xkAbUm8_k40)

A demonstration on how to get more deterministic values out of LLMs and use the output shape to stream dynamic components in your frontend

- Event context: AI Tinkerers NYC March 2024 Meetup — 2024-03-27 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_xkAbUm8_k40

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