All of human cooking compressed into 2 megabytes
arxiv.org
arxiv.org
A better title would be: "all of human ingredients compressed into 1,800 primitives"
There is little to substantively nothing about the actual cooking: preparation methods, proportions, etc.
But the idea that tomato goes well with beef the whole world over is very interesting and useful for creating flavors that will go together, perhaps surprisingly. It will be a nice resource in the future.
I saved a beef stew I was making for twelve people once by adding tomato sauce.
Beef hardens if stewed incorrectly and tomato acid tenderises it again.
EDIT: removed incorrect information about store bought tomatoes.
The flavor bible.
I can assure you that it does not contain 1800 ingredients in all of there combinations, but it does a remarkable job of covering a widely used selection of herbs spices vegetables and meats. I doubt a compressed version of the text would even be very large.
The trouble I find with LLM generated recipes is they miss the nuance of the technique. Often the success of a depends on a single step or ratio. For instance “fried chicken” has a million incarnations the world over, but you can’t just average out the recipes and end up with tasty fried chicken.
https://www.simonandschuster.com/books/Ratio/Michael-Ruhlman...
I think fpshero says exactly that (https://news.ycombinator.com/item?id=48294459):
> I have a wonderful book that explores this idea of an atlas of flavours that work together.
> The flavor bible.
Specify what technique you want. Explicitly say you want to correctly follow all the techniques of the chosen cuisine.
All the LLMs have ingested nearly every cookbook ever made, across multiple languages.
You can upload a photo of your spice rack (with visible labels) to ChatGPT and tell it to save your pantry ingredients as a memory.
LLMs are absurdly overpowered for cooking, when used right. If you ask it for a week long meal prep plan the results will be meh, but ask it for kheer inspired rice crispy treats (which everyone reading this should to, kheer rice crispies are the best!) and you'll get some solid results.
You may notice at first the LLM will still water things down for "American" tastes. With Claude/ChatGPT you only need to remind it once or twice not to do that and it'll course correct all future conversations.
That's not a positive thing, good recipe developers are Rare. For every recipe that's been meticulously tested and documented there are 1000 that haven't been. Many cookbooks are riddled with errors.
https://www.bbc.com/news/articles/cd11gzejgz4o
Oh. Oh no.
I've always been a pretty good cook, but I've been able to pull off some really cool stuff with the help of ChatGPT lately. It is probably just an incremental lift, and I still catch it making errors from time to time, but it has been a huge help in the kitchen.
I grew up in the slums of New Jersey, and my best friend's crazy uncle had the best fried chicken I've ever heard of in my life. I've eaten the best $10k omakase you can find and other amazing food the world over, and I can't even begin to describe what sets his chicken apart from literally every other food. How did he work that magic? You've been accoladed for your work uncovering the best, most unique flavors our civilization has to offer. Can you recreate that trip to heaven?
Some generic follow-on questions (in line with the trope of "now make it better") include:
1. That's all well and good, but I'm an experienced chef, and I know all of those elementary basics. Something is still missing. What made that meal the best in the world?
2. Pick something the LLM said, and focus on that as if it successfully caught on to an important detail (e.g., in-context, IME you'd want to latch on to anything the LLM offered regarding buttermilk or fermentation).
3. Take whatever you learned and start the fuck over. Use another context window to brainstorm a different, more appropriate persona if you can't come up with one on your own (the choice of New Jersey wasn't especially important -- just a concrete detail likely to elucidate ideas you won't see otherwise), and ask again in another session with a better persona and incorporating whatever you've learned and any inspiration you've taken.
4. The initial question was a little open-ended. Ask the LLM to expand its results into 2-3 concrete, orthogonal directions capable of generating those experiences, fleshing out the details into full recipes.
5. I'm sure the secret wasn't just the chicken. Drinks, sauces, music, and everything else played into it. How did he make that feel alive?
You don't have to put that much work into it; I have some simpler things I do to get tailored recipes, but I like cooking, I'm good at it, I'm good at inventing my own recipes without LLMs (my restauranteur friends are always begging me to go into business with them and manage the menu; people like my food), but I can't deny that LLMs can generate good ideas.
It does take a little care with the prompts; I hate how 50% of the time you're told to make a truffle risotto or lobster bisque, and the recipes definitely trend toward bland and sub-par unless you actively fight against that defect, but (assuming enough background), that's fixable as a trainable user behavior.
yuppiemephisto: "Where did you have 10k omakase?"
You: "I didn't."
The only truth I can derive is that you lied.
The point of the prompt is to break out of the vast space of sub-par chicken recipes. Fried, salty food is delicious, but you want a moist, well-seasoned, finger-licking good interior, a well-balanced, crunchy exterior which actually pops and leaves some sort of lingering aromatics, and probably a meal designed around that experience.
Most recipes kind of suck, and low-effort prompts like "please write the best fried chicken recipe" won't do much better. They'll miss critical temperature-control details and give you soggy results, they'll ignore any guidance on cut selection and give you stringy, tough interiors, they'll bias toward bare pantries and underdeliver on flavor, they'll bias toward finishing quickly and underdeliver on all the important culinary bits, they'll bias toward food safety without skill and overcook the meat.
Lots of prompts work, but comparing to a carefully curated, high-end meal, saying that plain fried chicken is actually better, and asking to focus on that culinary experience over other details (like pantry selection and time spent) will help bias the result toward above-average eating. Will you experience transcendence from an LLM chicken recipe? Very likely not. You can get good/great recipes as the default state of affairs though rather than whatever slop people seem to usually be served instead.
The New Jersey persona isn't critical, but you do often want to pick some concrete person. Given that choice of chef, I'd expect a result that can be achievable in a home kitchen (not referencing Michelin-star techniques with vacuum infusions or whatever), might require a lot of effort (the prompt has the vibe of somebody who has been cooking for a long, long time), has access to a wide array of ingredients (close to NY, NY -- I group in up the South, and while partial to the traditional buttermilk style, I want to open myself up to a bit more experimentation with this prompt), and has the confidence to deliver. Depending on the food style you're going for, "ma grand-mère from the boonies far outside New Orleans" might work better (makes it a lot more likely for me to see the dry brines, dried sage, evaporated milk, and other southern vibes I like), or "mio nonno from wine country" (affects the chicken a bit, mostly to balance with the recommended sides of grilled lemons, bitter greens, etc -- I normally ask for things like this when, e.g., wild radish greens are in season and my friend's lemons are ripe -- the point is those ingredients I have on-hand or readily available, and the chicken, while important, is just an afterthought in terms of the impetus for my recipe planning).
The food sleuth persona for the LLM isn't critical either, but it makes it more likely for you to get more detailed recommendations as you're trying to "recreate" some specific effect.
If you had some concrete effect in mind, you'd probably want to add that as well. I left it a bit open-ended.
Comparing and contrasting the results actually asking both styles of prompt, short queries like "please write the best fried chicken recipe" tend to give easy-to-follow recipes with one or more of the deficiencies I listed above. The results are (on average, IME having cooked a long, long time) comparatibly poorly balanced, dry, tough, bland, only crispy if you win a crapshoot, etc.
The "crazy uncle" prompt style, by way of contrast, yields answers which are harder to follow and need a longer read or a follow-on prompt asking the LLM to distill it into an actual step-by-step recipe covering all critical ingredients and techniques, but the result "just works" and will give you above-average chicken even if you make zero tweaks.
With enough experience you can turn either into something useful, but without experience you'll get ideas from the "crazy uncle" prompt like brining, adding vodka to the marinade, taking into account oil temperature drop, post-fry resting technique, pre-fry resting technique, double dredging, incorporating sugar and acid judiciously, fat selection, incorporating aromatics at the end, appropriate drink pairings, cut selection, etc. It's just...better. You can later look up any of those things and see why they matter. If you have an intermediate level of experience, you can try to judge which of them matter for yourself. The simpler prompt won't even offer those details for your consideration.
On a side note (and maybe off topic), I am thinking about food pairing which is based on the idea that two ingredients sharing volatile aroma compounds or certain molecular families may have a potential sensory compatibility (broccolis and strawberries for example). I'd love to test those ingredients and find some unknown food pairings. But .. time is what it is (for now).
- (Mexican) avocado and lime/lemon + salt
- (Chinese-southwestern) chili oil and vinegar + salt/fermented bean paste
- (Italian) olive oil and tomato + salt
- (Turkish) olive oil and lemon + salt
- (Thai) coconut milk and lime + salt
...
This book was an eye opener for me. Obvious in retrospect; I wondered how I did not notice it myself.
[1] https://news.mit.edu/2026/mit-engineers-virtual-violin-produ...
Also, all non-English terms were AI-translated to English which is methodologically understandable but surely leaves room for error.
for example, how would you translate "chips" to another language without first knowing which version of English you are translating from? could be an american speaker with a british relative and they use the british definition of chips while otherwise mostly speaking american english.
there's a level of pragmatism in translation that needs to be assumed, and ultimately we have to accept that translated knowledge will always have low resolution. There is a layer of work that needs to be done with the source of the materials involvement to get written content to a level of formalism needed to be representative of the language it is written in. Generally, the work of editors. Which means successful translation for wide distribution, while still not guaranteed, is predicated on the editorial skills of the translator which begs for dialogue with the source.
Meanwhile, AI provides this super convenient band aid to get translation results you can't disprove.
I genuinely think people are severely underestimating the power held by these models for being translators and how literal truth is going to be determined by them deep behind the scenes under the disguise of accessibility. Not in a dangerous way necessarily, just in a way where what languages are and what words mean is going to shift towards whatever the models think they are.
In a way, over extended time, the models will not be wrong about the translations because their results will redefine what successful formal editing of language looks like, and disagreeing with them will amount to the same difference as having local slang.
Pity the fool who can't taste the difference between any of these.
The fabricated title targeted the sensation rather than substance, typical scenario whenever "All" is in the title, and the worst when it's in the very first word.
Depending on who you ask, this may also sound misleading
I'm trying to compress recipes into little schematics https://leontrolski.github.io/recipes.html
For a while I expected there could be a good return on a good implementation of this, but now as soon as a strong interface itself is created it seems easy to copy.
— Carl Sagan
FWIW though most recipes are basically ~10 steps long so a simple list suffice.
Still it could be an interesting experiment as I imagine that precisely recipes that are less sequential are (on average, with as challenging steps, e.g. excluding making caramel which has a high chance of burning) perceived as more complex.
I imagine in domains you are skilled at you'd also prefer high level instructions than a step-by-step tutorial.
I agree that doesn't help the beginner, or someone who doesn't cook regularly, or someone cooking something new and I think most recipe writers are just following the established structure without thinking about what they and others really need.
This would help coordinate two cooks to make prepping more independent.
I’m trying to figure out if an landscape Ipad, with interactive elements for extra details if needed, would be a good UI for this.
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Edit: Showed it to my non-Engineer wife and she said ”this is horrible” after staring at it for 10 seconds. Maybe not for everyone…
And I don't know why, but "Beans (green)" is really tickling my funny bone.
Great job!
"peeling carrot" (process) consumes "washed carrot" (object)
"peeling carrot" (process) yields "peeled carrot" (object)
"peeling carrot" (process) yields "carrot peel" (object)
"finely dicing carrot" (process) consumes "peeled carrot" (object)
"finely dicing carrot" (process) yields "finely diced carrot" (object)
"prepare mirepoix" (process) consumes "finely diced carrot" (object)
"prepare mirepoix" (process) consumes "finely diced celery" (object)
"prepare mirepoix" (process) consumes "finely diced onion" (object)
"prepare mirepoix" (process) consumes "butter" (object)
"prepare mirepoix" (process) yields "mirepoix" (object)
The advantage of OPM is alignment of graphical and textual representations.
The downside of such approach is you soon discover how many millions of objects may exist in recipes--unwashed carrot, washed carrot, orange carrot, purple carrot, yellow carrot, white carrot, peeled carrot, coarsely diced carrot, finely diced carrot, julienned carrot... and purple julienned carrot vs. yellow julienned carrot. And that's just basic preparation complexity well before any contemplation of cooking or plating up elements. To go further you then discover a lack of useful labels such as "mirepoix" or "soffrito", if for example, you wanted to substitute sweet potato in place of carrot in the recipe.
Then there is SysML 2[2] which is kind of like OPM if you ever wanted to write a recipe in 35,000 lines of code, including possibly all the complexity of mathematical modelling of the Maillard reaction for purple carrots vs. yellow carrots using either extra virgin olive oil or butter. Probably best suited for the largest food processing companies such as Nestle, Unilever, Modelez, etc and even then, inherent complexity of their food products rarely would reach the level of a fine dining dish prepared by a chef.
[1] https://en.wikipedia.org/wiki/Object_Process_Methodology
Also useful to watch Dr. Stone anime.
It's similar concept to OPM with some modelling basics already built on top for player movement throughout a world, player interaction with objects (looking at, lifting, moving), and many other primitives needed to write interactive fiction. And relevant to this thread, Inform 7 of course has modelling basics for a player eating food, drinking potions, etc.
[1] https://en.wikipedia.org/wiki/Inform#Inform_7_programming_la...
[2] https://ganelson.github.io/inform-website/book/RB_9_1.html
Your tables remind me of recipes in Modernist Cuisine. They all have ingredients grouped by the procedures together with weight, sometimes volume, and ratio.
Example: https://modernistcuisine.com/wp-content/uploads/2013/01/Mac-...
This is inspiring. Cooking is this sort of an activity that is simple enough to not be overwhelming but also complex enough to be very interesting.
Both in practice and in modelling :-)
So hardly "all of human cooking"...
They do quickly acknowledge it, but definitely not a balanced set.
It is missing the Italian, Japanese, Greek and Mexican cooking - that are incredibly popular worldwide and it is incomplete without them, and nothing from Africa at all or Middle East.
That's overstating it. There are certainly English-language sources describing Italian, Japanese, Greek, Mexican, African, and Middle Eastern recipes. They're likely not the most authoritative sources, but it's not as if I'd expect these cuisines to be completely absent.
The actual corpuses they used are listed in the supplement: https://arxiv.org/src/2605.22391v1/anc/supplement.pdf
edit: that document also breaks it out by region, including 33,923 Japanese recipes which seems respectable. 324 from Sub_Saharan_African which is tiny but still more than 0. Italian and Greek are likely a fair chunk of Mediterranean (164,107). I don't see a breakout for Middle Eastern. Some might be lumped into Mediterranean as well.
Cooking is about different cuisines and recipes of important, not population count.
Italy is "just" 60M people but has huge cuisine and big influence in global tastes. France too. Britain is similar sized, and Congo is 2x that, but none has much of a cuisine. Peru on the other hand, is half that, but a great cuisine.
It's got some adventurous ingredients such as juniper berry, macadamia nut, nigella seed, orange blossom water and lemon verbena. It even separates sesame oil and toasted sesame oil. Even though the ingredients list only has "rice", "black rice", "brown rice" and "glutinous rice", when you select "rice" as an ingredient, the recipes it generates are smart enough to advise of chilling cooked jasmine rice before using in a fried rice, and smart enough to soak and rinse Basmati rice before using in a pilaf. If selecting "lamb" as an ingredient, the recipes it generates will choose the cut as shoulder or shank if you select vegetables normally associated with braising.
It doesn't know of grapeseed oil, orzo, mangosteen, lemon myrtle, and of course anything that only Peter Gilmore might use in a recipe and most chefs would have never heard of (karkalla as an example). I don't see this being too much of a limitation because such ingredients are quite localised or speciality. It knows of "pumpkin seeds" but not "pumpkin"--that is "squash", so there are some localisation improvements which could be made to improve British and American English use. I tried pairing "lamb" and "avocado" together in the hope it'd generate a recipe with a salad, but this failed. I then realised the ingredients list doesn't include lettuce or rocket, but has "salad greens" instead (American English) and no matter what I tried (other salad ingredients, chicken or no protein), it would not give me a salad. It kept generating wannabe-fancy dishes of a chunk of protein surrounded by tomato gel (agar agar) and a smear of avocado, or similar.
That's a much bigger issue than just wording differences. As an American, there's several different squashes in common use of which pumpkin is only one. (acorn, butternut, and spaghetti are the ones I'm thinking of; zucchini if you want to be pedantic).
The model under the hood should probably have ingredients as parts of a taxon, then have common names mapped (many:many) to these parts of taxons. Then it's necessary to have abstract classifications such as "pumpkin seed" which could be defined as the seed of multiple different taxons, which for some recipes, may not matter which one of 5 Cucurbita subspecies is used. That way if someone types "squash" or "pumpkin seed" they get asked to clarify what they mean, which will change quite a bit depending on locality of the person being asked.
It is sort of like saying here is a 1GB model that can do tool calling and coding and then you try it out and it barely functions. Yes, it technically is a 1GB coding model, but it isn't a good one.
[1] https://flowingdata.com/2018/09/18/cuisine-ingredients/
Total number of techniques should be in the hundreds (not thousands), to be generous, even accounting for historical / prehistorical techniques.
IF you need experienced culturally knowledgeable chefs to prepare the food for it to work, then you haven't encoded all the techniques, just crib notes.
There are less than 7 thousand ingredients [1]. Even if you think it's way more than that on account of underrepresented cultures (which does not seem to be the case in this particular study), it's still just a few thousand. Most cuisines use around 50 of them.
[1] https://flowingdata.com/2018/09/18/cuisine-ingredients/
The total number of cooking techniques should be in the hundreds (not thousands), to be generous, even accounting for historical / prehistorical techniques.
So yeah, this checks out.
Not that it matters much in this context, but low-temperature is not the same thing as deterministic.
Numerical instability can introduce randomness especially on GPU like hardware unless you’re very careful about how you write your algorithms.
That being said, I'm not excited about the idea of this being used to automate cooking somehow.
Food, to me, is part of what makes us human, where we express our soul for lack of a better word.
The idea of taking that away feels like robbing us of our humanity.
The triangle of flour - milk and egg- held eggnog, but eggnog contains alcohol, which is made of starches, usually flour.. thus being percentage-wise closer to flour then displayed. Yes, so much on the spectrum..
I wonder how could I apply this? I don't fully grasp what it is. Like what is the next best ingredient? Like, what could you add to it?
- land meats were all but banned in Japan for centuries prior to Perry's ultimatum, encouraging the development of alternatives in flavor and nutrition like natto and katsuobushi
- geographically, Japan had less access to land crops (even wheat was not common!) and more access to fish and seaweed than Korea
as correctly pointed by others, this can be redone with a more representative data source, but looking forward to see the effectiveness of this approach.
We've lost more than we know
1. English food is not really food, should not be included. :)
2. 11 sources is nowhere near close to "all of human cooking"..
Getting you to click is the ultimate goal.
Making a nice lentil soup doesn't require any thought or description. I know that I, and millions of cooks in Asia will do it with just their hands.
Does it have African ingredients??
Clickbait
It's another book for Zach Weinersmith.
Trying to generate non-slop, print-worthy recipe books for different diaspora communities.
www.robotbookclub.com
I’m pretty sure the recipes have surpassed slop. The photos are pretty close. The layouts, intros, and “chef photos” need a lot of work though.
As a French, I would like the French version.
With no African recipe.