The Prototype feature allows teams to test and evaluate different LLM configurations, prompts, and parameters in a controlled environment before deploying to production. This crucial step helps identify potential issues early, optimize performance, and ensure your LLM application meets your specific requirements.
To get started with Prototype, please follow the Quickstart guide.
Risk Mitigation: Identify potential hallucinations, biases, or inaccuracies before they impact users
Performance Optimization: Compare different models, prompt strategies, and parameters to find the optimal configuration
Cost Efficiency: Test and refine your applications to optimize costs
Evaluations: Leverage Future AGI’s evaluations to assess different aspects of your model performance Learn more →
Data-Driven Selection: Choose the winning prototype version based on key parameters such as evaluation scores, cost efficiency, latency etc. Learn more →
Seamless Production Transition: Move from prototype to production with minimal friction while maintaining full observability Learn more →