Chipotle macro tracking should handle the whole bowl.
Chipotle is macro-friendly, but it is not always easy to log. A chicken bowl can change fast depending on rice, beans, cheese, sour cream, guac, vinaigrette, chips, and portion size. Forge AI lets you describe the full order instead of searching item by item.
Log Chipotle in Forge AITry: "Chipotle chicken bowl with white rice, black beans, fajita veggies, corn salsa, cheese, sour cream, guac, and chips."
Details that matter
Base
Burrito, bowl, tacos, salad, white rice, brown rice, or no rice.
Toppings
Guac, cheese, sour cream, queso, salsa, corn, lettuce, fajita vegetables.
Extras
Chips, queso, vinaigrette, double protein, drink, or eating half.
What most trackers get wrong
The most common mistake is logging a generic "chicken bowl" and losing the calorie-driving add-ons. Guac, chips, queso, cheese, and sour cream can matter as much as the protein. A good tracker should keep those details visible.
Forge AI can use source-backed restaurant logic where available and AI estimation when an order is custom or incomplete. The user still gets the fastest path: type the order once, review the result, adjust if needed.
Better Chipotle prompts
Good prompts include the base, protein, toppings, and extras. "Chicken bowl" is usable, but "chicken bowl with white rice, black beans, fajita veg, corn salsa, sour cream, cheese, guac, and half the chips" is much better. If you ate only part of the bowl, say that directly.
Chipotle is also a good example of why food logging should be correctable. Portions vary by location and server, so a believable estimate plus an easy adjustment is more useful than pretending the first number is perfect.