Generative AI API for Developers: Stop Shipping "Successful" Failures

Generative AI APIs enable applications to request model-generated text, images, video, extraction, or retrieval-assisted answers, but developers remain responsible for choosing the correct capability, defining acceptance criteria, validating outputs, and managing the user-facing workflow. The guide emphasizes selecting models based on exact input and output support, latency, cost, data handling, integration requirements, and operational constraints rather than broad model names or consumer product claims. Using an asynchronous image-generation example, it recommends keeping credentials server-side, persisting task IDs, polling existing jobs rather than blindly resubmitting them, validating downloaded files for format and dimensions, and reviewing images for prompt compliance such as object counts, placement, unwanted additions, and usable composition. It distinguishes transport success, task completion, and business acceptance, advises bounded retries and clear recovery states, and stresses tracking costs per accepted output rather than per request because rejected generations, review, storage, and processing affect the true cost of a feature. Before production deployment, teams should protect access, constrain inputs and spending, persist job state, manage delivered assets and retention, monitor performance and acceptance rates, and maintain regression tests when models, prompts, or settings change.

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