Perceived exertion interpretation in AI coaching requires understanding that RPE is subjective and context-dependent — the same effort feels different on different days, in different conditions, and at different points in a training session. AI coaching that incorporates this context into RPE interpretation produces more accurate training load assessments than coaching that treats RPE as a fixed scale. This concept covers contextual RPE interpretation as a nuanced training coaching skill.
Perceived exertion interpretation is how AI coaching tools translate subjective effort ratings — like RPE (Rate of Perceived Exertion) scores or descriptive language such as 'felt exhausted at mile two' — into actionable training adjustments. Rather than relying solely on objective metrics like heart rate or pace, AI can use your qualitative feedback to understand how your body is actually responding to a given workload.
For people who don't use wearables or who want their subjective experience to drive their training, this concept bridges the gap between how you feel and how your plan should adapt. AI makes perceived exertion data useful by helping you structure and consistently interpret it across sessions over time.
After each workout this week, log a one-sentence effort description and an RPE score from 1–10. At the end of the week, paste all five entries into Claude and ask: 'Based on my RPE scores and effort descriptions, identify whether my training load this week was appropriate, too easy, or too hard, and suggest one adjustment for next week.'
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
Explore related journeys or tell Peri what you're working through.