The AI feedback in your check-ins is only as good as the data and instructions it receives. Here's how to make it more accurate and personalized.
1. Enable All Relevant Data Sources
Go to AI Check-Ins → AI Prompts tab. Each AI prompt template has checkboxes for which data the AI can access:
Client Information — name, age, goal, starting weight
Nutrition Data — weekly calorie and macro averages
Weight & Body Composition — weight trends, body fat, lean mass
Workout Performance — workouts completed vs scheduled, volume
Compliance Status — traffic light scores and coach notes
Recent Conversations — last 5 messages from client chat
Weekly Calorie Data — daily calorie breakdown for the week
Current Macro Targets — live data pulled from Trainerize API
AI Form Submission — latest form answers (within 7 days)
The more data sources you enable, the more context the AI has to generate accurate, personalized feedback.
2. Set the Right Data Timeframe
Each AI prompt template has a timeframe setting:
This Week — uses data from Monday to today (best for mid-week check-ins)
Last Week — uses data from the previous full week (best for Monday reviews)
If your Monday check-in uses "This Week", it will only have 1 day of data. Switch it to "Last Week" for a full week review.
3. Keep Client Status Updated
The AI adjusts its tone and recommendations based on the client's status:
Active — normal coaching feedback with workout and nutrition analysis
Injury — AI acknowledges the injury and avoids celebrating workout performance
Vacation — AI adapts expectations and focuses on maintenance
Sick — AI focuses on recovery, not performance
If a client is injured but their status is still "Active", the AI will give inappropriate workout feedback. Keep statuses current in the Compliance Tracker or client notes.
4. Write Custom AI Prompts
The default "AI Coach Feedback" prompt works well for most cases, but you can create custom AI prompts for specific use cases:
Click New Prompt
Write your instructions for the AI
Select which data sources to include
Each custom prompt generates its own placeholder (e.g.,
########{{my_custom_prompt}})
Use this for separate mid-week check-ins (shorter, motivational) vs full weekly reviews (detailed, analytical).
5. Use Coach Notes for Context
Add coach notes to clients in the Compliance Tracker. When the "Compliance Status" data source is enabled, the AI reads these notes and factors them into its feedback.
For example, adding a note like "Client is training for a marathon — prioritize carb intake" will help the AI give more relevant nutrition advice.
6. Configure Compliance Rules
The AI uses your compliance rules (Settings → Compliance Rules) to evaluate whether a client is on track. If the defaults (within 35% calorie deviation = green) don't match your coaching style, adjust them. The AI will use your custom thresholds.
7. Ensure Clients Are Logging Data
The AI can only analyze what's been logged. If clients aren't tracking:
No food logging → AI can't comment on nutrition
No weigh-ins → AI can't track weight progress
No workout logging → AI can't evaluate training
Encourage consistent logging. You can even use the Sunday check-in form approach to prompt clients to check in weekly.
8. Watch Out for Placeholder Typos
If you're using custom placeholders in your templates and see raw text like ########{{my_placholder}} appearing in the output — it's a typo. The placeholder name must exactly match what's shown in the AI Prompts tab. Fix the spelling and it will resolve.