3 items across 1 sections
Operational pattern for handling structured output from AI APIs (Gemini, GPT, Claude) in production. Covers the failure surface when AI output is used as data: JSON parse failures, schema drift, missing fields, type mismatches, markdown code fence wrapping, and the architectural patterns that make AI-driven data pipelines robust against model output variation.
Operational reference for running Gemini AI in production via Firebase Cloud Functions. Covers: structured output enforcement, JSON parse failure handling, 429 rate limit UX design, server-side key isolation, cold start latency mitigation, Node runtime requirements, and the three-part prompt architecture that produces reliable structured output across calls.
Design for AI-native operational retrieval: semantic search, debugging lookup, failure pattern retrieval, and entity relationship queries for the AI Execution Lab knowledge base.