How to track macros accurately (without losing your mind)

Most people who give up on tracking macros do not give up because it is hard. They give up because it got tedious, and because somewhere along the way they stopped believing the numbers. Both problems are fixable, and mostly with habits rather than willpower. Here is what actually matters, roughly in order.

Weigh your food. In grams.

A digital kitchen scale is the single biggest accuracy upgrade available, and it costs less than a month of most fitness subscriptions.

Eyeballing works fine for things that are mostly water. A handful of spinach that is off by 40 g does not change your day. Eyeballing falls apart on dense food: oil, peanut butter, cheese, nuts, rice, granola. A generous tablespoon of olive oil instead of a level one is roughly 60 extra calories, and four of those across a day quietly erase a deficit you worked for.

The practical version: put the bowl on the scale, tare it, add the food, log the number. Tare between ingredients and you never do arithmetic. Weigh the dense things carefully, stay relaxed about vegetables. That is not a compromise, it is just where the error lives.

Serving units are where errors quietly creep in

Most logging mistakes are not "I ate more than I thought". They are unit mistakes. The classics:

• Logging the per-serving column when the package serving is 30 g and you poured 70 g.
• "1 cup" of anything chopped, packed or loose, which varies by half again depending on how you filled it.
• "1 serving" of a food where nobody, including whoever created the entry, remembers what one serving was.
• Raw versus cooked. Rice roughly triples in weight when boiled. Log 150 g of cooked rice against a raw entry and you have invented a few hundred calories.

GreaseFit ships around 45 serving units, from grams and millilitres through cups, tablespoons and ounces down to slice, piece, can, jar and scoop. That is genuinely useful for logging a slice of bread without weighing it. But units are a convenience layer, not a source of truth.

So when you create a food, define its base serving in grams or millilitres, straight off the label, and let the friendlier units sit on top. Then a slice is a real weight rather than a vibe. And put raw or cooked in the name. "Rice, dry" and "Rice, cooked" as two separate foods will save you more grief than anything else you do in five minutes.

Log before you eat, not at the end of the day

Logging at bedtime is a memory test, and you will fail it. Not dramatically. You will just forget the milk in the coffee, the bite off someone else's plate, the second spoon of peanut butter. It adds up, and the log still looks fine.

The better reason is leverage. If you log the meal before you eat it, you can still change it. You see that dinner leaves you 40 g of protein short and you fix it while fixing it is possible. Logged afterwards, the same information is bookkeeping. Log before, and tracking turns from a report into a decision.

Build a library of the thirty foods you actually eat

You eat the same food over and over. Most people cycle through about thirty items for most of their calories: the coffee, the yoghurt, the bread, the chicken, that specific oat milk.

Searching a generic database every day is where accuracy goes to die. Ten entries come back for the same product with ten different calorie counts, you pick a different one each time, and now your week is noise.

Build the library once instead. In GreaseFit that means creating your own foods, with the full micronutrient editor if you care about the detail, entered carefully from the package in front of you. It is real work in week one and a few taps forever after.

Barcodes make the second time free. The scanner checks your own registry, so you scan an unknown barcode once, the app offers to create the food, and the barcode stays attached to it. Next time it is one scan. The point is not a giant public database. The point is that your regular shop becomes instant. How the pieces fit together is on the features page.

Recipes: let the serving count do the arithmetic

Home cooking is where most logs fall apart, because a pan of chilli is not a serving of anything.

Build it as a recipe once. Weigh the ingredients as they go in, including the oil, which everyone forgets and which matters most. Set the serving count to the number of portions you will actually divide it into. GreaseFit divides the totals by that serving count when you save, so a portion is a real portion. Log one and move on.

If your portions are visibly uneven, weigh the whole cooked dish and split it by weight instead of by eye. And set the preparation method honestly, because fried and boiled are not the same food.

Consistency beats precision

A number that is wrong by the same eight percent every day is far more useful than one that is exact on Tuesday and wild on Thursday. You are not using the absolute figure for anything. You are reading the trend, and a trend only works if your method holds still.

So pick your entry for a food and keep using it. Pick one macro preset and run it long enough to learn something. Pick one reference standard and stay there. GreaseFit offers EU, US, UK and WHO reference intakes, and they disagree with each other, which is normal. What is not useful is switching between them and wondering why your iron number moved.

Same entry, same unit, same method. Boring is the feature.

Restaurants, other kitchens, and honest guessing

You cannot weigh a restaurant plate, so stop trying to be accurate and start trying to be unbiased. Assume more oil and butter than you would use at home, because there is. Estimate portions against things you have weighed before, which is a quiet payoff of the library habit.

GreaseFit's AI photo estimate helps here. Point the camera at a plate, a label or a recipe page and it pre-fills the editor. Treat it as a starting point, because that is what it is. It cannot see the oil in the pan or the butter under the steak. Read what it produced, correct the parts you know are wrong, then save. An estimate you corrected beats a precise number attached to the wrong food.

The photo estimate, the label scan, the recipe scan and the AI chat share one daily pool: five credits a day free, twenty-five on Supporter, reset each calendar day in your timezone. The recipe scan also needs Supporter, or a new account's thirty-day trial. So it is not the tool for your daily oats. Save it for meals you cannot reconstruct. And when you have no idea at all, log something anyway. A rough entry is data. A blank day is not.

Tracking to learn, or tracking forever

Most people should track intensely for a few weeks, not indefinitely. What you buy with a strict month is calibration. You learn what 30 g of oats looks like in your bowl. You find out you eat far less protein than you assumed, or that the calories were hiding in things you never counted as food. That knowledge does not expire when you close the app.

After that, plenty of people track loosely: dense foods and protein sources, ignore the rest. Others log only when a goal changes. Both are fine. The sign the learning phase worked is that you can look at a plate, guess it, and be close.

The short version

• Buy a scale. Weigh the dense things.
• Define your foods in grams, and label raw versus cooked.
• Log before the meal, not at midnight.
• Build thirty foods once instead of searching every day.
• Use a recipe with a serving count for anything cooked in a pan.
• Hold your method still. The trend is the point.
• Use AI estimates for meals you cannot reconstruct, then edit them.
• Track hard for a few weeks, then track like a normal person.

More on how any of this works is on the FAQ, and there are other posts on the blog. Creating an account is free, and the paid tier changes two things: that daily AI quota, and access to the AI recipe scan. New accounts get 30 days of the scan. Recipes you build by hand are free either way.