Optimizing LLM Performance for Financial Intelligence
Financial AI use cases demand high accuracy with minimal hallucinations or unsafe responses—without compromising security, privacy, or compliance. At scale, latency and cost matter too, and teams must tune the right balance for each application before production.
This example is a practical reference for managing those trade-offs efficiently and a jumpstart for your own implementation:
How to Apply This to Your Data
This workflow demonstrates how you can operationalize "Outcome Engineering" for your own proprietary financial data. By testing different data, architecture and retrieval “knobs” side-by-side, you can engineer the right outcome for your AI agent.
