The OpenAI API call itself takes about ten lines. The real work is everything around it - streaming the response so the UI doesn't freeze, handling errors without showing users something terrifying, writing prompts that produce consistent output, and making the whole thing feel like a feature rather than a science experiment.

I've integrated OpenAI APIs into a live SaaS platform for content generation and built an AI chatbot into a real-time analytics dashboard. Both shipped. Both got used.

OpenAI API integration - GPT-4, GPT-4o, Assistants API

Streaming responses so output appears progressively instead of all at once

Conversation state and message history handled properly across sessions

Prompt engineering for consistent, predictable output from the model

Error handling and fallback UI when something goes wrong (and it will sometimes)

Rate limiting and cost-aware usage patterns

Full chatbot interface - message thread, typing indicators, streaming, input handling

AI content generation embedded into existing product flows

Works with new or existing React and Next.js apps

The AI won't always do exactly what you expect - that's just the nature of LLMs. But the integration will be solid enough that when it doesn't, your product still looks professional.