Much like humans, AI often makes implicit assumptions. This can often lead to errors. Many such errors are caused by implicit reasoning, where the model attempts to do more than the task requires but misses a critical piece of data. These errors can be eliminated using self-correcting workflows that combine AI with rules-based validation.
The self-correcting workflow uses the ‘learning by example’ approach that works so well for humans, first by providing few-shot samples (practical training) and then correcting any residual errors (supervision).
In a new article by Alexander Sokol and the Nvidia team, learn why AI-based free-form text workflows often fail, how you can adopt a self-correcting approach when building LLM-based automation for financial workflows, and how combining AI with rules-based error correction can help you achieve near-perfect accuracy in trade entry for financial ‘what-if’ analysis.
