•Automated Prompt Engineering: Optimizes prompts using algorithmic feedback loops to improve quality and reduce failure patterns.
•Continuous LLM Benchmarking: Tracks prompt variants, evaluation metrics, and test datasets in real time.
•Cost & Latency Tuning: Refines prompt instructions to reduce API token usage while maintaining model accuracy.