Use case · RAG and retrieval
Finance intelligence
Financial AI needs high accuracy with minimal hallucination, without compromising security, privacy, or compliance. At scale, latency and cost matter too, so each application needs its own balance before production.
The experiment
Test how well an agent answers complex financial questions, to improve customer-facing chatbots for banks and financial institutions.
- Dataset
- FiQA, a public dataset of financial questions and analysis.
- Agent
- A retrieval agent that finds the single most important piece of information for a banking or investment question, then answers it correctly and in context.
- Knobs tested
- ChunkingRetrieval depthRerankingGenerator model
What the experiment showed
- 01Reliable answers with fewer hallucinations.
- 02Answers stay auditable back to their sources.
How RapidFire helped
Every configuration in one run
Compared the knobs side by side on the same hardware, instead of one run after another.
Live results
Scored each version on slices of the eval set as it ran, so decisions came early.
Weak runs stopped early
Cut directions that were not working and put the compute behind the ones that were.
Winners cloned
Cloned the strongest run and changed one factor at a time to find what drove the gain.
Apply it to your data
- 1Swap in your own filings, research notes, and policies.
- 2Test data, architecture, and retrieval knobs side by side.
- 3Pick the configuration that balances accuracy, latency, and cost for your application.