One challenge I'd add is earning user trust beyond model accuracy. In fintech, even a technically correct AI response isn't enough if it can't explain why it reached that conclusion or provide an auditable trail for compliance.
We've seen this come up in projects at IT Path Solutions where retrieval quality, guardrails, and human review for high-risk actions often end up being just as important as the model itself. Reducing hallucinations is only part of the equation the bigger challenge is building systems that are reliable, traceable, and predictable under real-world conditions.
Curious how others are balancing explainability with keeping latency low in production.
Well said. In fintech, trust comes from explainability and auditability as much as accuracy. We've seen the same shift where guardrails, reliable retrieval, and human review become just as important as the model itself. Balancing that with low latency is definitely one of the biggest production challenges.
One challenge I'd add is earning user trust beyond model accuracy. In fintech, even a technically correct AI response isn't enough if it can't explain why it reached that conclusion or provide an auditable trail for compliance.
We've seen this come up in projects at IT Path Solutions where retrieval quality, guardrails, and human review for high-risk actions often end up being just as important as the model itself. Reducing hallucinations is only part of the equation the bigger challenge is building systems that are reliable, traceable, and predictable under real-world conditions.
Curious how others are balancing explainability with keeping latency low in production.