mastering macros with AI: make macro tracking easy with AI meal planning and ChatGPT meal plan prompts

[00:00:00] Emily Field: Welcome back to the Macros Made Easy podcast. I’m your host, Emily Field registered dietician, and today we’re diving into episode 59. Let’s start with this. AI won’t cook your chicken or log your lunch. You’re still the one running the show, but if you use it right, AI can save you 30 to 60 minutes a week and slash your decision fatigue.

 

[00:00:22] Emily Field: Macro tracking is one of the most efficient ways to learn what your food really contains. It’s nutrition education 101 that most of us never got in school, but let’s be honest, especially when you’re a beginner, the day-to-day part can feel overwhelming between planning meals, checking labels, and balancing your diary, sometimes it feels like a full-time job.

 

[00:00:43] Emily Field: That’s where AI can come in. If you know how to ask the right questions and when to double check its answers, it can take a lot of the busy work off your plate. By the end of this episode, you’ll have 10 real ways to pair AI with your macro routine. Know how to sanity check results so you know they’re accurate, and be able to spot the red flags that tell you when it’s time to take a step back and use your own brain.

 

[00:01:07] Emily Field: And because I want you to actually use what you learn, I’ve created a free resource for you, 30 chat GPT prompts to make macro tracking easier and faster. These are plug and play prompts that you can literally copy paste and start using today to simplify your macro tracking. I’ll share how to grab that at the end of the episode.

 

[00:01:27] Emily Field: First, I wanna talk about how to use AI to help with your nutrition, specifically with tracking and planning your macros. But here’s the key thing to keep in mind. AI is a sidekick, not a coach. I think of it like the sous chef in a restaurant kitchen. Amazing at chopping, organizing, prepping. But I’m still the head chef.

 

[00:01:48] Emily Field: I am the one who tastes the food, decides what’s gonna go on the plate and make sure it works for the person who’s actually gonna eat it. So AI helps me move faster. It helps me generate ideas, test options, and streamline the process, but it doesn’t replace the experience and context that a coach brings.

 

[00:02:05] Emily Field: Let’s walk through three stories to show you exactly what I mean. We’ll start with Wanda. Wanda is a runner. She’s training for marathons and logs 40 to 50 miles a week on paper, she looks like your classic small female endurance athlete, so when she used ai. This is what it saw. Small frame plus female plus high mileage equals must want fat loss.

 

[00:02:29] Emily Field: So AI output a neat 1600 calorie plan that promised both performance and leanness. But here’s what AI didn’t know. Wanda was constantly cycling between Undereating during those training blocks and overeating in the off season. She’d crash mid-afternoon, feel outta control around food, and her iron labs were trending low.

 

[00:02:50] Emily Field: When I looked at her, I saw a woman who wasn’t fueling enough to support the miles she was running as a coach. I saw the human behind the stats, a history of restrictive eating, clear energy deficits, and nutrient red flags. So instead of cutting her down, we did the exact opposite. We raised her calories, bumped carbs on her long run days, and added iron rich foods.

 

[00:03:13] Emily Field: She’d actually eat and enjoy. And the result, her right times got faster. Her cravings calmed down, and she finally recovered well after training. Same stats, but totally different plan. That’s the difference between AI seeing patterns and a coach seeing the person, and that leads us to the next big point.

 

[00:03:31] Emily Field: Even when AI gives you something that looks polished, the quality of the output depends entirely on the quality of the input. Let’s talk about Dana. Dana is a working mom with two kids in sports and a full-time job. She’s super disciplined. If you give her a plan, she will follow it to a T. When she first used ai, she asked, give me macro friendly dinners.

 

[00:03:56] Emily Field: AI saw a broad, vague request, so the output was gorgeous. Pinterest style recipes, 45 minute prep times exotic spices and meals, her kids wouldn’t touch On paper, they looked healthy, but in reality they were useless for her. Dana had two kids to shuttle to practice a husband working late and 20 minutes to get food on the table.

 

[00:04:18] Emily Field: That’s when we shifted the lens. As a coach, I can see the real life constraints, limited time picky kids, an oven and an air fryer, and the need for leftovers. So we changed the prompt. I have 25 minutes. Two kids who won’t eat spicy food, 45 grams of protein, 55 grams of carbs, and 15 grams of fat left for the day.

 

[00:04:40] Emily Field: Give me four dinners with a low fat swap and tell me exactly how to log them in chronometer. The output this time is perfect. Sheet pan meals un under 25 minutes, 35 to 45 grams of protein, each leftovers for lunches and no spicy ingredients. Dana went from stressed out to consistent because it wasn’t about getting a meal plan.

 

[00:05:01] Emily Field: It was about telling AI the reality of her life so that the answers fit her world. That’s the second lesson. Outputs are only as good as the inputs. A coach knows which inputs actually matter and how to phrase them, so you get something useful. But even with better prompts, there’s another trap. Assuming AI’s numbers are accurate, that’s where people can get burned.

 

[00:05:23] Emily Field: Which brings us to Mary. Mary is a classic high achiever. She lifts five to six days a week, tracks her food religiously, and wants results to show her effort. So she asked AI to give her a day of eating and AI saw an active woman who wanted to lose fat, so it output a color coded day of meals that looked Instagram worthy.

 

[00:05:43] Emily Field: 1300 calories, a hundred grams of protein balanced across the board. It looked perfect, but it wasn’t working. Mary was cold all the time. Her sleep was a mess. And despite all her effort in the gym, her body composition really wasn’t changing. So we dug deeper. As a coach, I saw a mismatch between inputs and reality.

 

[00:06:04] Emily Field: When we cross-checked her actual food against nutrition labels and weighed portions for a week, we saw the problems under logged fats, inflated protein numbers from products that didn’t really match the labels, and a calorie intake way too low for her training load. The fix. We rebuilt her food logs with verified entries, bumped her calories up, and made sure her protein sources were legit.

 

[00:06:28] Emily Field: Within weeks, Mary felt like a different person. Her sleep improved. Her training was stronger, and she started seeing the body composition changes she’d been chasing. When it comes to accuracy and macro tracking, there’s a hierarchy of what you should trust first. At the very top are the actual nutrition labels of the food that you see in front of you and the USDA database.

 

[00:06:48] Emily Field: Those are regulated, vetted source that give you the most reliable numbers. Next is your food scale, because weighing your food gives you a true measurement instead of guessing at portion sizes. Then comes your tracking app. Chronometer pulls directly from verified databases like the USGA, which makes it highly reliable.

 

[00:07:07] Emily Field: MyFitnessPal, on the other hand, allows user generated entries, which means you’ll find duplicates, inconsistencies, and sometimes flat out wrong data. Both apps can be helpful, but you need to know the difference. Chronometer leans on accuracy while MyFitnessPal leans on convenience and volume of entries.

 

[00:07:24] Emily Field: Either way, they’re only as good as the data that you select inside of them. And finally at the very bottom of the hierarchy sits ai. It’s great for generating ideas and speeding up your process, but it should never be your primary source of truth for nutrition data, AI doesn’t verify accuracy. It just reorganizes information it finds.

 

[00:07:45] Emily Field: So when in doubt, start with the label. Double check with your scale, log it in your app, and treat AI as the helper, not the authority. That order keeps your data clean and your progress moving in the right direction. So to wrap up here, when you put these three client stories side by side, there’s a big picture.

 

[00:08:05] Emily Field: AI is a phenomenal sous chef. It can chop, organize, brainstorm, and make your life easier, but it doesn’t taste the food. It doesn’t know if the plan will actually work for you, your body, your history, your stress, your lifestyle. That’s where coaching comes in. A coach sees the whole picture, understands your context, and knows how easy or how hard it will be to actually apply the recommendations in your real life.

 

[00:08:30] Emily Field: So use ai, absolutely let it make you faster and more efficient, but let a coach or your own growing and self-awareness decide what’s realistic, sustainable, and effective for you. That’s how you get the best of both worlds. Using AI to help with food choices is like sending an assistant to the grocery store.

 

[00:08:48] Emily Field: They’ll bring back a list, but you still need to check it before you eat. Using AI to help you with your food choices is a lot like sending a brand new assistant to the grocery store. They’ll bring back a cart full of items, but it’s still on you to double check before you cook or eat. Did they grab the right brand, the right size?

 

[00:09:08] Emily Field: Did they actually read the label correctly? That’s the same way to think about ai. It generates lists, meal ideas, or grocery suggestions in seconds, but it doesn’t know if those choices are accurate or practical for you. The goal isn’t to let AI overwhelm you with noise. It’s to make it work for you in a way that saves time and reduces friction.

 

[00:09:27] Emily Field: And the best way to do that is to use a simple, repeatable search flow that keeps things both fast and accurate. So here’s how that works. Number one is to ask narrowly. Instead of asking, what should I eat at Applebee’s? Narrow it down. List five macro friendly proteins at Applebee’s with at least 30 grams of protein.

 

[00:09:47] Emily Field: That specifically helps AI cut through fluff and give you options that actually work for your tracker. I had a client who traveled a lot for work and dreaded restaurant meals when she started prompting AI this way before heading out, she no longer wasted time scrolling through a massive menu. She knew exactly which meals lined up with her protein goals.

 

[00:10:08] Emily Field: Next, I want you to require structure. Ask AI to organize answers in a table with proteins, carbs, and fats, and maybe a suggested swap. For example, return is a table with protein, carbs, and fats, and one easy swap to lower fats or carbs. This makes the output scannable instead of a wall of text. It’s like giving your assistant a template so they can’t come back with scribbles on a napkin.

 

[00:10:33] Emily Field: This is reminiscent of Dana’s story, one of my busy, disciplined, but overwhelmed clients. So she used this approach for weeknight dinners by asking AI for structured swaps. She avoided that late night macro Tetris that left her frustrated. Number three is to ask for sources from ai. Good prompts include site menu items or brand pages used.

 

[00:10:55] Emily Field: If AI can’t show you where it got its numbers, you have no idea if they’re real or pulled from a random blog. Sources give you confidence in the accuracy or tell you when it’s time to dig deeper.

 

[00:11:06] Emily Field: So when you spot these, default to your tracker or known foods list instead. Number four here is to watch for red flags. Some signs that AI is going off the rails include no sources, provided numbers that look suspiciously round, like 50 grams of protein, 50 grams of carbs, 50 grams of fat, maybe impossible claims, like 50 grams of protein in a tiny side dish.

 

[00:11:31] Emily Field: When you spot these default to your tracker or your known foods list, AI can be a huge time saver, but only if you learn how to guide it well and then double check what it gives you. Think of it like that brand new grocery store assistant. You want clear instructions, a clean list back, and then you still take a quick look before you start cooking.

 

[00:11:51] Emily Field: The repeatable flow is simple. Ask narrowly, so you cut through the fluff and actually get options that work for your macros Require structure, so the output is organized and scannable instead of overwhelming. Ask for sources so you know if the data is trustworthy and watch for red flags like missing sources, perfectly round numbers or impossible claims.

 

[00:12:13] Emily Field: When you follow these steps, AI becomes a really powerful sidekick. It gives you ideas and reduces decision fatigue while you stay in control of accuracy and application. Remember, AI’s job is to reduce friction. Your job is to build the results. Use it to support your plan, not replace it. So let’s shift gears and get tactical here.

 

[00:12:35] Emily Field: I wanna walk you through some of the most practical ways to use AI in your day-to-day macro tracking. Think of these like little shortcuts times when AI can make things faster, easier, or less overwhelming. Now remember, the ground rule. AI is your sidekick, not your coach. It’s here to chop, prep and organize so you can spend more time actually living your life, not fussing over food math.

 

[00:12:56] Emily Field: I’ll read you 10 or so examples you can put into practice today. And if you want the full list of 30 ready to go prompts, you can grab that free download in the show notes. Let’s say it’s the end of the day and you look at your app and you’ve got 30 grams of protein, 20 grams of carbs, and 10 grams of fat left.

 

[00:13:14] Emily Field: Instead of scratching your head, you could ask ai, I have 30 grams of protein, 20 grams of carbs, and 10 grams of fat left. Give me six snack combos. Using foods I likely have at home, return as bullets with grams per serving. You could take this one step further and ask it to include one shelf stable option for travel days, so you’re not stuck without ideas in the future.

 

[00:13:36] Emily Field: If you ever feel stuck before going out to eat, AI can really shine here. You can ask, I’m going to a Mexican restaurant and want about 40 grams of protein and less than 50 grams of carbs in my meal list. Five orders and simple swaps like tortilla to a lettuce, wrap beans on the side, and gimme quick loose tracking estimates for each.

 

[00:13:59] Emily Field: You’ll still need to verify that against the menu of your actual restaurant or another verified nutrition menu from a Mexican restaurant, and then adjust it in chronometer. But now you’re walking in with a bit of a plan instead of winging it. You can use this for grocery lists. So let’s talk about grocery shopping.

 

[00:14:16] Emily Field: Instead of wandering the aisles, you can have AI do the heavy lifting. Try create a five day grocery list for 130 grams of protein, at least 10 minute breakfast, 20 minute dinners. Bundle items by store section. Add three backups for nights. I’m tired. This prompt, make sure your list is structured. It guarantees that you’ll hit protein each day and you’ve got built-in fail safes for busy nights.

 

[00:14:43] Emily Field: Let’s say you struggle with carbs, have AI map out your batch prep, so you might ask list easy carb sources I can batch in 30 minutes on Sunday. For each, give the cook method yield per half cup and storage notes. That way you’ve got prepped potatoes, rice, or oats, ready to plug into meals instead of scrambling when you’re hungry.

 

[00:15:04] Emily Field: When you’re on the road, AI can save you from that gas station roulette, so you might try packable non-refrigerated protein sources for three days on the road. I need at least 90 grams of protein per day. Add serving sizes. It’ll remind you of the things like jerky, protein powder, tuna packets, and you’ll avoid the trap of living on trail mix and boiled eggs alone.

 

[00:15:27] Emily Field: Here’s another example. Sometimes you’re hitting protein, but running high on fat, you can prompt AI with, I want higher carbs. Same protein. Suggest lower fat swaps for my usual lunches. Keep taste and texture similar. Give one-to-one substitutions that might look like Greek yogurt instead of sour cream or chicken breast instead of chicken thighs.

 

[00:15:49] Emily Field: Same meal, but adjusted to fit your day. I love the last one. What’s in my fridge rescue? One of my favorites for the end of the week would be to prompt, here’s what I have. Canned salmon, rice, frozen broccoli tortillas, Greek yogurt, build four dinners that have at least 35 grams of protein in under 15 minutes.

 

[00:16:09] Emily Field: With macros per serving, it’s like chopped, but AI gives you the winning combos. You save money, reduce waste, and still hit your targets. Now that’s just a taste of what AI can do to make macro tracking faster and easier. Remember, these prompts don’t replace a coach, but they do make the process smoother so you can stay consistent without overthinking.

 

[00:16:29] Emily Field: If you want the full list of my 30 ready to go chat GPT prompts, I put them together for you in one download. You can find the link in the show notes, grab it, and start using AI as your sous chef in the kitchen and in your nutrition routine. AI can give you a lot of ideas, but it doesn’t live in your body and it doesn’t always get the numbers right.

 

[00:16:48] Emily Field: That’s why you’ll still need to run its answers through a few common sense filters. These are some quick ways to keep yourself safe, accurate, and confident. Number one is portion plausibility. Always sanity check the serving sizes. If AI suggests that a quarter cup of nuts fits perfectly into your fats left for the day, pause and double check.

 

[00:17:08] Emily Field: Nuts are very calorie dense and that small looking portion might actually blow past your fat target. When in doubt, crosscheck the label or with your food logging app. Number two is protein reality. Please be skeptical of protein numbers that seem too good to be true. Most single food items don’t pack 50 grams of protein unless you’re talking about a double portion or a protein shake.

 

[00:17:32] Emily Field: If you see an unusually high protein count, verify it. It’s better to catch an exaggeration than to undereat your protein because of faulty math. Number three, method matters. Cooking methods make a big difference. Grilled chicken breast has a very different fat content compared to fried chicken, and even raw chicken breast has a very different macro content than cooked chicken breasts.

 

[00:17:55] Emily Field: AI won’t always clarify that unless you ask, so build it into your prompt, specify whether it’s grilled, baked, sauteed, or fried. Otherwise, you might log the wrong version and end up with misleading data. Number four, source over summary. Whenever possible, go straight to the brand’s website, the USDA database, or the actual food label instead of relying on AI’s summary of someone else’s blog.

 

[00:18:20] Emily Field: Post blogs can be great for ideas, but they aren’t regulated. The source itself will always be more reliable. And lastly, body feedback. At the end of the day, your body is the ultimate truth teller. If AI’s quote, perfect plan leaves you wiped out cold all the time, or constantly hungry, that’s your physiology waving a red flag.

 

[00:18:41] Emily Field: Numbers on paper don’t override real life signals. Remember, AI is great at ideas, but you are the editor that’s the heart of this whole conversation. AI can save you time and reduce friction, but you still need to use your brain and your body as the final filter. AI is powerful, but it can also become a crutch or worse, a source of confusion if you lean on it too heavily.

 

[00:19:06] Emily Field: Sometimes the smartest move isn’t to ask another question. It’s to take a step back and trust the tools and signals that you already have. One sign is when your first instinct is to open a chat window before you even glanced at your food diary or checked in with your hunger cues. AI doesn’t know you woke up hungrier today because you trained hard yesterday, or that you didn’t sleep well last night.

 

[00:19:27] Emily Field: Only you do. Your body and your food log are always the more reliable starting points. Another red flag is when you spend more time generating lists than you do actually eating. If you’re collecting 20 different snack options, but still haven’t made lunch, that’s a problem. AI should reduce decision fatigue, not create more of.

 

[00:19:47] Emily Field: And sometimes AI just makes things more stressful. If you close your laptop feeling more anxious, second guessing numbers, or worrying about being perfect, it’s not helping you anymore. In those moments, leaning on simple strategies like repeating meals you already enjoy or using your known foods list, well that’ll serve you much.

 

[00:20:06] Emily Field: Better than chasing flawless plans. I’ll give you a personal example. When I cook HelloFresh meals, I usually tweak them. I add a little bit more protein here, hold back some of the higher fat ingredients there. I’ve tried asking AI to run the macros on those edits. And honestly, it’s more trouble than it’s worth.

 

[00:20:24] Emily Field: The serving math gets messy. The totals don’t add up, and I’ve wasted time. I could have just spent entering the recipe into chronometer myself. The lesson is this, AI works best when you keep things simple. The more elaborate the ask, especially if it involves math, gymnastics, the more likely it is to fall apart.

 

[00:20:42] Emily Field: You don’t need flawless calculations to make progress. You need practical, close enough numbers that you can trust combined with consistency over time. Now if you’ve noticed yourself over relying on AI, asking for lists, instead of eating, stressing over numbers or going down math rabbit holes, you don’t need to quit cold Turkey.

 

[00:21:01] Emily Field: You just need a little reset. Here’s a simple three part plan. Number one. The one prompt rule, decide ahead of time how often you’ll use ai. So for example, only one AI assist per meal, or just one total for the day. That way it’s a time saver, not a time suck. If you need dinner inspo, ask once, pick an option, and then move on.

 

[00:21:22] Emily Field: This stops the cycle of chasing the quote, perfect answer, and gets you eating instead of endlessly planning. Number two is the five minute timer. If it takes more than five minutes to verify an AI response, it’s probably not worth it At that point, pivot to a tried and true option. Your known foods list, a go-to balance plate, or even something as simple as cottage cheese and fruit.

 

[00:21:44] Emily Field: If you’re trying to recalculate an entire HelloFresh recipe like me with protein edits and the math is just getting messy, set a timer. If it’s not resolved by then, save yourself the frustration and just log it your usual way. And lastly, number three, the repeat plate power on busy days ditch novelty.

 

[00:22:02] Emily Field: Choose two to three repeatable meals that you already know and love and cycle through them. This is where you get the most bang for your buck. Zero mental load. No second guessing and a hundred percent confidence in the macros. Think your favorite breakfast combo. A pre-GED lunch, you don’t mind repeating and one or two dinner templates that can flex depending on what’s in the fridge.

 

[00:22:23] Emily Field: Repeats give you stability and accuracy, so when you do want variety, you have the space for it without the stress. Pulling back from AI isn’t about rejecting technology, it’s about keeping the tool in its proper place. AI should lighten the load, not add pressure. These three simple rules keep you in the driver’s seat and remind you that progress comes from consistent.

 

[00:22:45] Emily Field: Good enough action. Not perfectly engineered plans. Alright, before we wrap up, I wanna do a quick q and a lightning round with some of the most common questions I hear when people start experimenting with AI for their nutrition. Think of this as like rapid fire myth busting, where I’ll show you where AI can help and where coaching still makes all the difference.

 

[00:23:05] Emily Field: Question number one, can AI calculate my exact macros? The short answer is it can estimate and I can personalize. That’s the difference maker AI is fantastic at crunching numbers. It can take your height, your weight, your age, and spit out a calorie or macro target, but those are just estimates. What AI doesn’t know is whether you’ve been undereating for years and slowed your metabolism, or how much lean muscle you actually carry because you strength chain.

 

[00:23:30] Emily Field: Or what your hormonal picture looks like. Perimenopause, thyroid health, PCOS. It also doesn’t know your dieting history, whether you’ve yo-yo dieted or restricted in ways that change how your body responds. Now, a coach takes all of that into account. I don’t just calculate numbers, I interpret them in the context of your life, your goals, and your physiology.

 

[00:23:51] Emily Field: That’s why two women with the same stats can come up with very different macro prescriptions. AI gives you a starting line, but coaching gives you the map. Question number two, what if AI contradicts my tracking app? The short answer is default to the label or the USDA database. Food databases inside apps are sometimes crowdsourced depending on the app, which means that the entries can be flat out wrong.

 

[00:24:16] Emily Field: AI can’t spot that. It just parrots back what it finds online. This is where your real brain and a coach’s eye can come in. That’s what I teach my clients Trust the label or the USDA database first, because those are regulated. Build your own known foods list inside chronometer or MyFitnessPal with reliable versions of foods that you eat often, and then start to notice patterns.

 

[00:24:38] Emily Field: If AI tells you a protein bar has 60 grams of protein, I’ll be the first to say that’s not realistic. Most bars land between 15 and 25 grams, so yes, AI can save you time, but a coach helps you build the discernment to know what’s accurate and what’s not. That’s the skill that builds confidence long term.

 

[00:24:57] Emily Field: Question number three. Is it cheating to ask AI for meal ideas? No, it’s efficient. One of the biggest reasons people fail with nutrition isn’t lack of willpower. It’s decision fatigue. Every time you ask what’s for dinner, you burn energy. That could have gone into your workout, your job or your family. And AI can generate a dozen macro friendly dinner ideas in 30 seconds.

 

[00:25:18] Emily Field: That reduces friction and keeps you consistent. But here’s the distinction. AI can give you lists. But a coach can help you decide which list actually works for your life. Maybe you need meals that your kids will eat, or options that travel well for work or higher carb meals to fuel your long runs. AI doesn’t know those nuances, but a coach does.

 

[00:25:39] Emily Field: So no, it’s not cheating. It’s smart. Use the tool but skip the judgment. Pair AI’s speed with coaching’s, discernment, and you get a very powerful combo. Less decision fatigue, more consistency, and faster progress. So that’s your lightning round. AI can estimate your macros, but it doesn’t know your body. It can give you food lists, but it doesn’t know your life.

 

[00:26:00] Emily Field: And it can suggest meal ideas, but it can’t decide what actually works for you. And that’s why I keep coming back to the same point. AI is an amazing sous chef. It can prep, chop, and brainstorm, but it’s not the head chef. Coaching or your own developing awareness is what makes the final call. And if you wanna make things easier, I put together a free resource for you.

 

[00:26:21] Emily Field: That’s the 30 copy paste chat, GPT prompts to make macro tracking faster and more efficient. These are the exact kinds of prompts that I’ve been walking you through today. So grab the link in the show notes, download them, and start using AI as your sidekick instead of struggling on your own. Today you learned how AI can make macro tracking easier, faster, and less overwhelming.

 

[00:26:42] Emily Field: We talked about how better prompts lead to better outputs, how AI can help you with snack ideas, grocery lists, and travel plans, and how it can reduce the friction that often gets in the way of consistency. But the big takeaway is this, AI reduces friction. You build the results. It’s a tool to support your plan, not replace it.

 

[00:27:02] Emily Field: Because while AI can crunch numbers and generate ideas, it can’t interpret your body’s signals, your history or the realities of your life. That’s where coaching and your own awareness comes. So if you’re ready for more than just shortcuts, if you want expert eyes on your data and a plan that actually fits your midlife metabolism, you’ve got two options.

 

[00:27:23] Emily Field: One, join us inside of Eat to Lean my group coaching program for women who wanna get leaner, stronger, and more confident without restrictive dieting. Or if you’re looking for a more personalized starting point. Get a custom macro calculation where my team and I set your exact macro targets based on your stats, your goals and your life, AI can give you speed.

 

[00:27:43] Emily Field: Coaching gives you strategy, and together that’s where the magic happens. Okay.