Ship an AI Feature on Claude — the gap between a demo and production

Everyone can get Claude to do something impressive in a chat window. Turning that into a feature inside a product — one that holds up when real users hit it with real inputs — is a different job, and it's where most AI features quietly fail: the bill creeps, an output hallucinates in front of a user, quality drifts and nobody notices for a month.

Ship an AI Feature on Claude is the path across that gap, as a $12.49 bundle.

What's inside

A spine — six phases from designing the feature to verifying it before users see it — plus five deep workflows:

• Build the Claude API integration — caching on the stable prefix, streaming, the current model, tool use; not a fragile first draft
• Tune cost + quality with an eval set — cut cost with quality measured at each step, not blind
• Add retrieval (RAG) when context isn't enough — ground answers in your data, and know when to skip it
• Observability + guardrails — see what the feature does in production, catch bad outputs before users act on them
• Pre-ship verification — the gate: eval set passes, adversarial inputs degrade safely, cost is observable

Why this matters here

The one thing that separates a shippable AI feature from a demo is that you can measure it — quality on an eval set, cost per call, what it did today. The bundle is built around that. Each workflow also references the free skills it leans on — claude-api-builder, llm-cost-and-quality-tuner, rag-architect — so the playbook hands you the tool at the step you need it.

See the bundle. It's one of three new outcome bundles — the others take you from idea to a hosted product and from build to launch.