Building on Moving APIs

Lesson preview

An AI API is a moving dependency, not a permanent contract. Before production use, record the endpoint, model ID, SDK package and version, whether the feature is preview or GA, the provider's deprecation page, and who owns migration. Pin dependencies — not assumptions. When a deprecation lands, run your golden eval set against the replacement before switching traffic. A concept being valid doesn't mean a vendor product will survive. 'Evals' as a discipline is essential; OpenAI's product called the Evals platform goes read-only Oct 31, 2026 and shuts down Nov 30, 2026. 'Assistants' as a pattern lives on; the Assistants API is gone. When you learn a technique, learn it in vendor-neutral terms first, then map it to whichever product currently implements it. That way a shutdown is a migration, not a rethink. For every AI dependency, keep a short record: endpoint, model ID, SDK package and version you tested, preview vs GA, link to the deprecation page, announced shutdown date, documented replacement, migration owner, and the date you last checked. This takes five minutes and turns a surprise outage into a calendar item. Pin the exact model snapshot and SDK version you tested so behavior doesn't silently change. g. older GPT-5/o3 snapshots on Dec 11, 2026; tts-1 and older transcription aliases on migration deadlines). Treat preview models as short-lived: great for experiments, risky for anything a client depends on.

Key terms in this module

Model deprecation

Model deprecation is when an AI provider announces that a model or API will be retired and sets a date after which requests to it stop working. Until then it still runs, but users are expected to move to a recommended replacement.

Model pinning

Model pinning is the practice of naming one exact, fixed model version in your code or settings, instead of a general name the provider can point at a newer model. It stops the model from changing under you before you have tested the new one.

Regression testing for AI

Regression testing for AI is re-running a fixed set of test cases after any change to a prompt or model, to check that what worked before still works. It catches changes that raise the average score while breaking one specific case.

Frequently asked questions

Who is "Building on Moving APIs" for?

This Advanced module in AI Builder is for learners who already use AI daily and are ready to build their first real workflows and integrations. It takes about 12 minutes and ends with a 4-question quiz.

Do I need to code?

No traditional programming is required. You will use no-code and low-code tools, and occasionally paste a short snippet that the lesson explains line by line.

What comes before and after this module?

Before "Building on Moving APIs", take "Personal Metrics & Review Ritual" (module 20 of AI Builder). It is the last module of AI Builder; continue with another volume in the curriculum.

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