5 Ways to Use AI to Cook Better at Home

"AI in the kitchen" has been marketed hard for three years, and most
of it is noise. Generative models inventing a recipe from scratch is
not a great idea. Inventing a dinner from what is in your fridge
sometimes works and sometimes suggests you add baking soda to chili.

Set aside the hype. There are a small number of places where AI
genuinely helps a home cook in 2026. Here are five, and then three
where it still does not.

## 1. Pulling recipes out of messy sources

This is the use case that pays for itself.

A cookbook PDF, a TikTok video, an Instagram Reel, a photo of a
handwritten card, and a blog with twelve popup ads — all of them
contain a recipe, none of them are structured data. Traditional
parsers handle clean recipe sites. They fall over on everything else.

Modern AI extraction pipelines handle the messy cases. They read the
image, transcribe the video audio, and produce a clean ingredient
list and numbered steps. The result is a structured recipe in your
library, not a link that will 404 in two years.

That is what /import does, and it is the single biggest time saver in
most home cooks' workflows. See /blog/save-recipes-instagram-tiktok
for the specific video case, or /blog/pinterest-recipes-not-working
for why structured trumps saved links.

## 2. Scaling recipes without breaking them

A recipe written for four does not scale cleanly to ten by multiplying
every number by 2.5. Leavening, salt, and liquid do not scale linearly.
Cook times change because surface area changes. Pan size changes
because volume changes.

AI-aware scaling knows that "1 teaspoon baking powder" per cup of
flour is a ratio, that "salt to taste" stays roughly linear but caps
out, and that a double batch of cake needs a bigger pan at a slightly
lower temperature. A dumb multiplier does not.

This is the second place AI earns its keep. Try it on a recipe at
/recipes — scale from 2 servings to 12 and watch what changes and
what does not.

## 3. Smart substitutions

"No eggs, what do I use" is the most-searched cooking question in the
world. The answer depends on the recipe — flaxseed "eggs" work in
brownies and not in meringue. Buttermilk swaps to milk-plus-vinegar
in pancakes but not in biscuits if you care about the tang.

A good AI substitution suggestion reads the rest of the recipe to know
what the ingredient was doing — binding, leavening, moistening,
flavoring — and picks the swap that preserves that role. It will also
tell you when there is no good substitute and you should cook a
different thing.

See /blog/convert-recipe-vegan-keto-gluten-free for how this shows up
in diet conversions.

## 4. Meal planning around what you already have

Traditional meal planners ask you to start from zero and add recipes.
That is fine Sunday morning. It is not fine at 5 pm on a Thursday when
you need to cook what is already in the fridge before it goes bad.

AI-assisted planning flips it: here is what you have, suggest three
dinners tonight. It is a search problem across your saved library
filtered by ingredient, and it is one of the cases where AI actually
beats hand-curated logic — because the fridge is always weird.

/meal-plan combines the week-ahead view with this "use what you have"
view. Either approach is valid depending on the day.

## 5. Nutrition estimation without the labels

Packaged food has nutrition labels. Home-cooked food does not. Looking
up twenty ingredients in a USDA database for every recipe is
unrealistic.

AI-backed nutrition estimation pulls database values per ingredient,
weights by quantity, and produces a recipe-level estimate in seconds.
It is still an estimate — see /blog/read-nutrition-labels for the
honest limits — but it is close enough to cook by, and it updates when
you scale the recipe.

## Three places AI does not help (yet)

- **Inventing recipes from scratch.** Generative models produce
plausible-looking recipes that have never been cooked. Some are
fine. Some recommend proportions that do not work. A recipe from a
tested source beats a freshly invented one every time.
- **Replacing taste.** AI cannot season for you. A dish that needs
"more salt and more acid" is usually obvious once you taste it, and
no amount of model tuning changes that.
- **Teaching technique.** Knowing when to pull a steak, how to tell
if dough is kneaded enough, how to read a sear — these come from
reps in your own kitchen. Video helps. Text helps less. An AI
"explain" feature is a supplement, not a substitute.

## The honest take

AI in the kitchen works best when it is invisible — extracting a
recipe in the background, scaling an ingredient list, suggesting a
substitute without interrupting you. It works worst when it is the
star of the show, inventing "dinner ideas" with confidence and no
experience.

Use it for the first. Be skeptical of the second. Try the import
flow at /import on a recipe that has burned you before and see how
much of the friction actually disappears.