OpenAI

OpenAI's model APIs are a practical foundation for AI features — assistants, chat, embeddings, and structured completions. I integrate them where their reasoning reliably earns the cost, not as a default for every problem.

My integration patterns focus on the contract: structured output via JSON-schema, tool/function calling, token budgeting, and prompt versioning. Fallbacks and evals keep quality predictable as models change under the product.

Because these APIs move quickly, I build the integration behind a stable interface so swapping or routing between providers is an implementation detail, not a rewrite.

The core affordance is the reasoning API — chat completions, assistants, and embeddings — and I integrate it where the model's judgement genuinely pays. The first question is always "does this task need an expensive reasoning call?", and the honest answer is often no.

Structured output is how a GPT call becomes reliable software. I constrain the response to a JSON schema so downstream code gets typed objects, not prose to parse — the difference between a demo that works and a product that runs.

Tool and function calling turns the model into an actor over the system. I define a typed tool surface and let the model invoke it, with explicit limits — so GPT decides when to act, and the system decides what it is allowed to do.

Cost and latency are engineered, not hoped. I budget tokens per request, cache where calls repeat, and route the cheap path to a smaller model — because an integration that burns the most expensive call on every trivial question is a bill, not a feature.

Prompt drift is a real risk as flavours of the model release. I version prompts with the code, tie them to an eval set, and review output samples on change, so a new model version cannot silently erode quality.

Provider abstraction keeps the door open. The integration lives behind a stable interface that can route between OpenAI, Anthropic, and Gemini at runtime — which is less about vendor-hopping and more about not being hostage to one provider's availability or pricing.

I ask first whether a task needs an expensive reasoning call. GPT is used where its judgement genuinely pays, not as the default for everything.

Structured output is how a call becomes reliable software. I constrain the response to a JSON schema so downstream code gets typed objects, not prose to parse.

Tool and function calling gives the model an actor surface. I define a typed set of capabilities with limits, so GPT decides when to act and the system decides what it may do.

Token budget, cache, request — cost is engineered, not hoped. Repeated calls are cached and simple work routes to a cheaper model, so the integration is a tool, not an open wallet.

Prompts are versioned and tied to an eval set. Output samples are reviewed on change so a new model version cannot silently erode quality.

Fallbacks and output validation catch the wrong-but-confident failure. Before an answer reaches a user, it must pass the schema and the checks.

A narrow provider layer keeps the door open. The integration can route between OpenAI, Anthropic, and Gemini at runtime, which is resilience rather than vendor lock-in.

The integration stays transparent and observable. Every call is measured — latency, tokens, and cost per request — so the model is a governed component, not a black box.

The honest test is whether a deterministic rule or search index answers better. I use the model for judgement, not for everything.