Prodigy Recipes

Prodigy's core bet is that annotation quality comes from fitting the tool to your task, not from a bigger SaaS dashboard. That shows up everywhere in the product. A custom recipe is a Python function decorated with @prodigy.recipe that declares its own arguments, loads its own data source, and returns a stream of tasks — you can swap the annotation interface, define custom HTML and JavaScript, and change which question gets asked. The docs list the components for building your own workflow scripts rather than only using the pre-built ones. For an ML engineer, that means a colleague with no front-end experience can still ship a working annotation task. The built-in coverage is broad: NER, span categorization, text classification, dependencies and relations, computer vision with image classification and segmentation, audio and video annotation including audio.manual, and prompt engineering recipes for LLM development such as ner.llm.correct. Active learning surfaces the most informative examples first, and a review interface plus task routing exist for managing quality across annotators. The llm plugin and the DSPy plugin push human feedback into automated prompt optimization. Where it fits: engineering-led teams with strict privacy rules — banking and finance, healthcare and biomedical, legal and insurance, media archives, research corpora — who are already in Python and comfortable running their own database and hosting. The vendor documents S&P Global in a high-security environment, The Guardian doing quote extraction from news, Nesta processing 7m job ads, and Love Without Sound recovering millions for music-industry law firms. Where it doesn't: teams that want point-and-click setup with no Python, buyers who want a fully managed cloud service with hosted scaling, groups that need to audit or modify the full source (it is not open-source, though you do get some parts of the source code), and distributed teams expecting simultaneous collaborative labeling — Prodigy is not built for that, and it deliberately does not provide user accounts or project management. You also install and maintain the database yourself. The trade for all this is control: nothing phones home, no data leaves your machine, models you produce are yours with no lock-in.