Personal LLM Health Analysis Tool
Multi-model AI orchestration for personalized health insights
Top outcome
Synthesized 100+ lab data types with medical context
Stack
Claude API · OpenAI API · Python · JSON Parsing
Overview
A sophisticated multi-model AI system that orchestrates Claude and OpenAI APIs to synthesize complex laboratory data with personal health context. The system features prompt versioning for reproducible analysis, intelligent context window management for handling large medical datasets, and cost-optimized API routing that selects the best model for each subtask. Built with a focus on accuracy, privacy, and actionable health insights.
The Problem
Integrating complex lab data with health context to generate personalized insights requires nuanced reasoning across multiple data types.
The Solution
Built multi-tool orchestration system using Claude + OpenAI APIs with prompt versioning, context management, and cost optimization.
Impact
- 1Synthesized 100+ lab data types with medical context
- 2Built reusable prompt templates for recurring analysis
- 3Managed rate limits, cost optimization, multi-API tradeoffs
Key Decisions
- Chose OpenAI for analysis tasks requiring nuanced medical reasoning
- Opted for Claude for specific data visualization tasks and backend architecture
- Implemented batch processing to avoid rate limit exhaustion
Lessons Learned
Prompt versioning is critical for production AI systems
Context window management requires careful planning for complex health data
Multi-model orchestration demands explicit cost/latency tradeoffs
Screens
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