APIs & integrations for the product leader

A model on its own does nothing for your product until something connects it to the rest of your system. That connection is an API call: a request sent, a response returned, over and over, millions of times a day in a real product. Everything that makes an AI feature feel solid instead of flaky — it doesn't time out, it doesn't double-charge a customer, it degrades gracefully when a vendor has a bad day — lives in how that connection is built. Most of this is not exotic AI engineering. It is the same integration discipline that has always separated a reliable product from a fragile one, applied to a new, less predictable kind of dependency.

This module teaches that discipline at the altitude a product leader needs: what the request-and-response contract with a model actually looks like, the practical mechanics of calling one (authentication, rate limits, streaming, retries), how to get data back in a shape your systems can trust, how to receive results without your product sitting and waiting, how to fit an AI call into a system that already exists, and how the emerging standard for connecting models to tools and data actually works. It hands off early and often to the deeper spokes elsewhere in this curriculum, because most of the mechanics here are not new — they're the API and integration craft your engineering team already knows, meeting a genuinely new kind of dependency.

The knowledge graph

An API call to a model is a contract, wrapped in the ordinary discipline of building reliable systems. Every lesson in this module hangs off this picture:

Read it in three passes. The contract: every call is a request in, a response out, and that shape is the foundation everything else builds on. Making the call work in practice: authentication, rate limits, streaming, and retries are the ordinary mechanics of any production API call, now applied to a dependency that is slower and less predictable than most. Fitting it into a real system: structured output, async delivery, latency budgets, and failure isolation are what turn "we can call a model" into "our product can depend on calling a model" — and the emerging MCP standard is changing how much of that plumbing you have to build yourself.

The lessons

• The request/response contract — what a call to a model actually sends and returns, and why that shape is a contract like any other.
• Calling an LLM API — authentication, rate limits, streaming, and retries: the mechanics of a call that actually works in production.
• Structured output & JSON mode — getting data back in a shape your systems can trust, without re-deriving the whole topic here.
• Webhooks & async patterns — receiving a result without your product sitting and waiting for it.
• Integrating into existing systems — latency budgets, idempotency, and keeping one dependency's bad day from sinking the rest of your product.
• MCP & standard connectors — the emerging standard for plugging tools and data into a model, and what it means for your integration strategy.

Each lesson pairs the mechanics with a 🎯 For the product leader briefing — why it matters, the decision it changes, the question to ask your team, and the risk if ignored — plus a diagram. Where a lesson touches deeper mechanics already covered elsewhere, it links out: to APIs & contracts for the general discipline, Structured output and Function calling for the reliability engineering, and Tools & function calling for MCP inside an agent's loop.

📌 Close out the module: Recap & real-world examples.