Build a RAG AI Coaching Layer on an Athlete's Own Activity Data

1. 1

   Design the RAG architecture

   Build a system that, at question time, retrieves the relevant slices of an athlete's own activity history and journal to ground a conversational answer, rather than training a model on their data. This retrieval-augmented approach is the whole architecture, and it is what keeps you compliant with platform data-usage rules. Get the retrieval and grounding right first.

2. 2

   Integrate the athlete's data sources

   Connect to the rider's activity platform and let them add voice-note journals and context, so the coach reasons from their real training, not generic advice. The value is that answers are grounded in this specific athlete's history. Support the data sources your riders actually use, and respect each platform's access rules.

3. 3

   Enforce per-request-only inference

   Commit architecturally to using the athlete's data only for per-request inference and never to train or fine-tune a model, because platform policies (Strava's November 2024 policy distinguishes permitted coaching tools from prohibited data-harvesting) require it. This is not just ethics, it is the condition of keeping your data access. Build so training on user data is impossible by design.

4. 4

   Make the coaching genuinely useful

   The rider should be able to ask real questions (why was that ride hard, how should I taper, what does my week look like) and get grounded, specific answers. Usefulness grounded in their own data is the product. Pair the RAG with sound training logic so answers are both personal and correct.

5. 5

   Launch as a privacy-first subscription

   Charge a subscription and lead with the privacy-first promise: your data is retrieved to help you, never used to train a model. That promise is a real differentiator with athletes wary of data misuse. Make the privacy architecture a selling point, not fine print.

6. 6

   Manage inference cost and compliance

   Per-request inference is a genuine recurring cost, so watch cost per active user against your subscription price. Stay current with the data platform's policies, because they define what you may build. Treating platform compliance and inference economics as ongoing work is how this model stays viable.

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