What generative AI development covers in practice
Generative AI development means building software where a language, image or speech model produces part of the output. The model is only one component. Around it sit retrieval over your documents, prompt and template management, permission checks, output validation, logging and a user interface that makes uncertain answers easy to verify. Whichever family fits, we begin with a narrow slice and real examples, so scope grows from evidence.
Most projects fall into a few families: conversational assistants for customers or staff, document intelligence that reads contracts or invoices, content and personalization engines, and developer tools that draft or review code. Each family has different failure modes, so we pick the architecture after looking at the task, not before. Starting from your own cases also exposes data quality problems early, when they are cheap to fix.
