AI Recipe Tools: What They Actually Do Well
AI is genuinely useful for some recipe work and actively dangerous for other parts of it. The dividing line is whether a wrong answer is embarrassing or illegal. This sets out where it helps, where it does not, and how to use it without creating a compliance problem. See FoodCore compared with Paprika.
Where AI genuinely helps
The common thread: these are tasks where a human immediately notices a wrong answer. Transcription errors show up when the quantities look odd; a bad substitution shows up in the bake.
Where it must not be trusted
Allergen declarations are the clear line. A model asked whether a product contains sesame will answer confidently from general knowledge about the recipe type, not from your supplier’s specification for the particular sesame-free seed mix you actually buy. It cannot know what is in your cupboard.
- Allergen determination — must come from supplier specifications, ingredient by ingredient
- Label generation — must be derived from recorded data, not generated prose
- Shelf-life setting — a food safety judgement about your process and storage
- Regulatory answers — useful for orientation, not for compliance decisions
The failure mode is specific and worth naming: an AI answer is plausible, fluent and unsourced, which is exactly the profile that gets accepted without checking. The allergen tracking guide explains why the data has to come from the ingredient record.
How FoodCore uses AI, and how it does not
FoodCore includes AI checks — 20 a month on Essentials, 50 on Growth, 100 on Core — and they are deliberately assistive rather than authoritative. They flag things that look wrong: an ingredient with no allergen data recorded, a costing that implies an improbable margin, a label missing an element.
What the AI does not do is decide what is in your food. Allergen data comes from the ingredient records you build from supplier specifications, and labels are generated from that data. A model never writes your ingredient list. That separation is the whole point.
On privacy: the only thing sent to our AI sub-processor is the supplier receipt or document you ask it to read. Customer records, orders and staff data are never sent. The sub-processor list sets out exactly who processes what.
A workable division of labour
- Use AI to get data in — transcribing a supplier spec, a handwritten recipe, a photographed pack label.
- Verify the numbers yourself. Quantities and prices are cheap to check and expensive to get wrong.
- Let software derive the outputs — cost, allergens, labels — from verified data.
- Use AI again at the end as a reviewer, not an author: what looks wrong here?
Frequently asked questions
Which AI tool is best for recipes? For transcription and rewriting, the general assistants are all capable. For costing and allergens the question is wrong — you want software that derives those from data, with AI checking rather than deciding.
Can AI write my allergen label? It should not. A label must be derived from recorded ingredient data traceable to supplier specifications. Generated prose is not traceable to anything.