AI workflows
AI Feeds API Patterns: Enrich Content Without Losing the Source
Design a bounded enrichment workflow for summaries and labels, with source evidence, review states, and sensible failure handling.
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AI context
Connected ideas across feed formats, practical engineering, and thoughtful AI workflows.
Trace the inputs that shape an AI-assisted result: source records, retrieved passages, task templates, and model revisions. Each is a different kind of dependency, and each deserves its own identifier and evaluation plan. Begin with a concrete question or editorial task, then follow the evidence required to complete it. The goal is not to send everything to a model; it is to provide the right material in a way that can be inspected afterward. These guides help you connect retrieval decisions to a repeatable, reviewable workflow.
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AI workflows
AI Model Feeds API: Build a Useful Model Release Watchlist
Track model releases with revision identifiers, model-card evidence, evaluation context, and a clear distinction between claims and measurements.
AI workflows
LLM Feeds API: Build a Source-Grounded Context Pipeline
Turn changing feed items into selected, traceable context using sensible chunking, retrieval tests, freshness rules, and citation checks.
Data & delivery
AI Prompts Feeds API: Version a Reusable Prompt Library
Treat prompts as versioned records with explicit inputs, validation rules, review notes, and a repeatable path from draft to release.