Full Stack

MyFitnessBuddy – AI Fitness Coach

MyFitnessBuddy acts as a 24/7 personal trainer and nutritionist. Unlike static fitness apps, it uses RAG to pull from a vast database of nutritional science and exercise physiology, tailoring every response to the user's specific biometric data, goals, and limitations.

Project typeIndependent build
StatusCompleted project
FocusFull Stack

Technology

AzureOpenAICosmosDBRAGPythonReact

System approach

  • Vector Database: Azure CosmosDB with vector search capabilities.
  • LLM Orchestration: Azure Prompt Flow managing OpenAI model calls.
  • Frontend: React interface for personalized fitness and nutrition workflows.

Challenges

  • Preventing hallucinations in health and fitness advice.
  • Handling complex, multi-turn conversations about diet adjustments.
  • Ensuring low latency in generating comprehensive weekly plans.

Solutions

  • Implemented strict RAG boundaries, forcing the LLM to ground its answers exclusively in the retrieved scientific literature context.
  • Used a specialized conversational memory buffer that summarizes past dietary restrictions.
  • Pre-computed embeddings for common workout routines to speed up the retrieval process.

Key outcomes

01

RAG-powered personalization

02

Azure AI integration

03

Context-aware health recommendations