03 / ENGINEERING CASE STUDY · Products
LiftCircle
A native SwiftUI training app that combines workout logging with a Python retrieval-augmented coaching backend.
01 / CONTEXT
The problem
Useful training advice depends on workout context. LiftCircle brings logging, including reps in reserve (RIR), together with personalized coaching, while also handling the everyday product requirements of authentication and subscriptions.
02 / SYSTEM DESIGN
Architecture
From workout context to personalized coaching
- Firebase · authentication
- StoreKit · subscriptions
- Gold dataset · coaching evaluation
03 / TRADEOFFS
Engineering decisions
Use retrieval to inform coaching
The backend uses retrieval-augmented generation and semantic chunking for workout analysis. Retrieval gives the coaching system relevant context to work from; output quality still requires evaluation rather than assuming that retrieved content guarantees a good recommendation.
Evaluate the backend as part of the product
The project includes gold dataset evaluation for the coaching workflow. Dataset composition, scoring criteria, and evaluation results are not published here, so this case study describes the evaluation approach without claiming a quality score.
Keep the training workflow native
SwiftUI brings workout logging and RIR input into the iOS app, alongside authentication and StoreKit subscriptions. These integrations make the coaching workflow part of a usable application rather than a standalone chat demo.
04 / VALIDATION
Evidence & measurement
This case study is based on the supplied résumé and the linked product website. Implementation details beyond that description, usage figures, and measured coaching outcomes are not publicly established here.
RAG coaching
The résumé describes a Python backend with semantic chunking and gold dataset evaluation for workout analysis.
View supporting referenceNative iOS experience
The résumé lists SwiftUI, RIR logging, authentication, and StoreKit subscriptions. The product website provides the public product reference.
View supporting reference05 / NEXT ITERATION
What I’d improve next
Project scope comes from the September 2026 résumé. The architecture is conceptual; the next steps are proposed improvements, not completed work.