Your Training Data Context

Stride AI builds a picture of you based on your history, current fitness, and lifestyle context. This allows it to personalise volume, intensity, and progression rather than following a one-size-fits-all plan. For example, if you have trained fairly consistently on Tuesdays, Thursdays, and Saturdays in the past, the AI will notice and build a similar calendar going forward.

Current metrics#

The AI starts with your current physiological profile to set appropriate targets and boundaries.

• FTP / CP - Used to scale workout intensities, define training zones, and estimate the load of each session.
• W' (W prime) - Informs high-intensity and repeatability work, especially for intervals above threshold.
• Weight - Used for watts-per-kilo calculations and to keep power targets realistic for climbs and longer events.
• HRV - Feeds into your readiness and recovery assessment so the AI can dial sessions up or down when needed. Useful for detecting oncoming illness or chronic fatigue.

Power curve#

Your power curve shows what you can do across different durations, not just a single FTP number. The AI uses it to understand your strengths and limiters (for example, strong 5-minute power versus weaker 30-minute power) and shapes intervals, progressions, and race-specific work around those gaps.

Historical load and fatigue#

Your training load and fatigue tell the AI how much work you can safely handle and how fast to progress.

• Load - The cumulative training load you have built up over weeks and months sets the baseline for future progression.
• Fatigue - Short-term fatigue trends guide when to push, when to hold, and when to schedule recovery blocks or lighter days.

Historical activities#

Past activities give the AI context about how you respond to different types of work, not just how much you train.

• Training Score (TS) - Quantifies the impact of each session so the AI can balance hard, moderate, and easy days.
• RPE (Rate of Perceived Exertion) - Your perceived effort helps reconcile how a workout felt versus how hard it was on paper. Submitting RPE after each session gives the AI better context. Stride can attach this automatically from Zwift, and Garmin head units support it out of the box.
• Comments - Notes about sleep, stress, pain, or how a session went give the AI richer context for future decisions. If you are self-coached, you can leave comments for yourself and the AI to inform future training.
• Session type and structure - Intervals, endurance rides, races, and group rides are all interpreted differently when forecasting load and adaptation.

Illness, holidays, and events#

Life context is critical so the AI avoids treating you like a robot. When you set up your calendar with events and constraints, the AI uses them to shape your plan:

• Illness - Time off or reduced capacity is factored in so the AI rebuilds gradually instead of dropping you back into full load.
• Holidays - Known breaks or travel windows let the AI front-load, de-load, or maintain as needed around those dates.
• Events and races - Past race dates, results, and comments help calibrate taper strategy and expected performance for your next key event.
• Availability and notes - If you have an important morning meeting, a busy afternoon, or other constraints, the AI can work around them. You can leave any kind of note for the AI to read.