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1d (edited) • 🟠 AI Practitioner
📊 Nutrition Tracker Part 2: Teaching ChatGPT Context Using Project Instructions
In Part 1, we started with a simple experiment.
I gave ChatGPT a basic food and activity log and asked: How did I do today?
The answer was generic. Why?
Because ChatGPT did not know enough about me.
It did not know my:
- Goal
- BMR
- Daily calorie target
- Protein target
It did not know what “good” meant for me.
That was the first real lesson:
- A generic chat produces generic answers.
- The next step was creating better context memory using Projects.
Step 1: Create a Dedicated Project
Instead of starting a random new chat every day, I created a dedicated ChatGPT Project for nutrition and activity tracking.
Why does this matter?
Because a regular chat is just a conversation. A Project is more like a focused workspace.
It gives ChatGPT a place to keep the work organized around a specific purpose.
For this project, the purpose was simple:
🍽️ Track food
🚶 Track activity
🔥 Estimate calorie burn
📊 Summarize the day
🎯 Help me understand whether I am moving toward my goal
To create the Project:
- Click Projects
- Click New Project
- Name it Nutrition and Activity Tracker
Step 2: Add Basic Project Instructions
Next, I gave ChatGPT a few simple Project Instructions.
Nothing complicated.
Just enough context to make the answers more useful.
Something like:
You are my nutrition and activity tracking assistant.My goal is to lose body fat while maintaining muscle.My estimated BMR is 1,800 calories per day. When I enter food, estimate calories and macros.When I enter activity, estimate calories burned.When I ask for a summary, show food calories, protein, carbs, fat, activity burn, net calories, and whether the day is trending toward a calorie deficit. Keep summaries concise.
That small set of Project Instructions changed the quality of the output because ChatGPT now had context.
To add Project Instructions:
- Open the Project
- Click the three dots
- Select Project settings
- Add your instructions
The Same Food Log Became More Useful
In Part 1, I entered something like this:
Today I ate:
Breakfast:
- 2 scrambled eggs
- 2 slices wheat toast
Lunch:
- Turkey sandwich
- Small apple
Dinner:
- Grilled chicken breast
- 1 cup white rice
- Mixed vegetables
Snacks:
- Protein bar
- Handful of potato chips
Activity:
- 7,500 steps
- 30-minute walk
Summary
Without instructions, ChatGPT gave a generic answer.
With a Project and basic Project Instructions, the response became more specific.
Try it now. Just follow the steps above.
It will now say something closer to:
Food Summary
- Calories: ~1,750
- Protein: ~115g
- Carbs: ~165g
- Fat: ~55g
Activity Summary
- 7,500 steps: ~300 calories
- 30-minute walk: ~120 calories
Total activity burn: ~420 calories
Net Calories
1,750 food calories minus 420 activity calories = 1,330 net calories
That is already much more useful than a generic response.
The Bigger AI Lesson
This is not just about nutrition.
The real lesson is context memory.
A basic chat can answer a question.
But a Project helps ChatGPT understand the purpose behind the work.
What is the objective?
What should it track?
What rules should it follow?
What should it not do, also known as guardrails?How should it report back?
Once those pieces are in place inside a Project, ChatGPT becomes much more useful because it is no longer responding in isolation.
It is working from defined instructions, clear rules, and your context.
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📊 Nutrition Tracker Part 2: Teaching ChatGPT Context Using Project Instructions
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