AI Workflow Engineering

Demos are easy now. The hard part is everything around the model call. What happens on the two hundredth concurrent request. What you do when it returns JSON that doesn't match your schema. How you know last week's prompt change actually helped rather than just changed things, and what the whole thing costs per month once real traffic hits it.

So most of my time goes to evaluation and failure handling rather than prompting. Retrieval that returns the right chunks, and a way to measure whether it did. Structured output with schema validation and a repair path. An eval suite you can run in CI. Caching, batching and model routing to keep the bill in range, and sensible fallbacks so a provider having a bad afternoon degrades your product instead of taking it down.

Usually this has to live inside a Django app you already have, so it also has to fit your auth, your background jobs and whatever observability you're running. In practice that's the real constraint, more than anything about the model itself.

This is for you if…

• You shipped an AI prototype and now need it to be reliable
• Nobody can tell you whether the last prompt change helped
• Your inference bill is growing faster than your usage
• You need RAG over your own data, done properly