Solutions
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:
01
Dataset
Public data source for financial questions and analysis (FIQA).
02
Agent
Designed to ensure that when a user asks a banking or investment question, the algorithm identifies the single most important piece of information to generate an answer that is both factually correct and contextually relevant.
03
Objectives
Tested how well an AI can answer complex financial queries to improve customer-facing chatbots for banks and financial institutions, providing reliable answers while maintaining auditability with reduced hallucinations.
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.
