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The best AI help here comes from understanding what each ingredient does, not just replacing it. Salt does more than taste salty: it suppresses bitterness and makes other flavors seem stronger. That is why the most reliable sodium cuts combine several moves. Use no-salt-added canned goods, add acid from citrus or vinegar, build aroma with toasted spices, garlic and herbs, and add some salt at the end, where it lands on the surface and is tasted first. A model can lay out these techniques and fit them to a specific dish. Sugar is harder to cut in baking because it is part of the structure. It holds moisture, tenderizes by interfering with gluten and egg proteins, feeds browning, and in cookies controls how much they spread. Many baked goods tolerate a modest cut, often cited as around 25 percent, but larger cuts change the texture. Fat carries flavor and makes food tender. Replacing some butter with oil lowers saturated fat, and fruit purees or yogurt can stand in for part of the fat in quick breads and muffins, at the cost of a denser crumb. The main caveat is the numbers. Large language models do not look anything up in a nutrition database by default. They generate plausible figures from patterns and can add totals wrong, confuse raw and cooked weights, or ignore how much of a marinade is actually eaten. For accurate values, use a tool that multiplies real ingredient weights against a reference source such as USDA FoodData Central. For context, the Daily Value for sodium on US nutrition labels is 2,300 milligrams. A common misconception is that a healthier recipe has to swap in processed "diet" products. Bigger gains often come from portion size, adding vegetables and fiber, and methods like roasting instead of deep frying.