Introduction
AI recommendations in Lynk are only as good as the information they’re built on. The system does not rely on assumptions or generic models alone—it learns from the data you actively record during your coaching workflow.
Clear, consistent inputs lead to clearer, more useful recommendations.
Core Inputs That Power Recommendations
Lynk AI combines multiple data sources to understand coaching context and learner progress.
Coach Notes
Coach notes provide qualitative insight into:
Performance observations
Challenges or constraints
Behavioural and focus-related signals
These notes help the AI understand why outcomes look the way they do.
Attendance Records
Attendance data reveals:
Participation consistency
Engagement patterns over time
Gaps that may affect progress
Irregular attendance often changes how recommendations are framed.
Progress Reports
Progress reports add structured evaluations through:
Holistic skill indicators
Technical skill assessments
Coach-written summaries
These act as formal checkpoints for AI interpretation.
Session and Batch History
Session history helps AI:
Identify long-term trends
Compare recent progress with earlier stages
Understand pacing and continuity
The more complete the history, the better the context.
Why Input Quality Matters
Clear notes lead to sharper suggestions
Consistent attendance marking improves pattern detection
Thoughtful reports strengthen insights
Incomplete or vague records limit recommendation accuracy.
Summary
AI recommendations in Lynk are built from coach notes, attendance, progress reports, and session history. The quality and consistency of these inputs directly determine how useful and relevant AI guidance will be—keeping the coach firmly at the center of the process.