Hyperparameter Optimizer for LLM and RAG Systems
Workflow Diagram
Task
The AI system determines optimal configurations for retrieval-augmented generation (RAG) pipelines through multi-objective optimization across safety (hallucinations) alignment (helpfulness) cost carbon and latency.
Intent
The aims of the AI system are to enable efficient safer RAG deployments improve helpfulness provide benchmarks for compliance and support informed trade-offs.
AI Workflow
Input
Document data model options and example queries are given to the AI system.
Process
The AI system optimizer evaluates configurations on five metrics and visualizes trade-offs.
Output
A user selects a configuration that balances priorities and can iterate on the configurations by modifying inputs.
Human Oversight Level
Human-Approved AI
Institutional Oversight Examples
- •Transparent documentation of optimization methods (organization best practice)
- •Regular benchmarking (organization best practice)
- •Users retain decision authority (organization best practice)
- •Safeguards to prevent unsafe configurations (organization best practice)
Risk
Some of the AI system risks include a potential over-reliance on metrics and that test datasets may not reflect real usage leading to suboptimal choices.