LLMs often provide confidence scores that don’t accurately reflect the reliability of their outputs. This leads to mistrust and inefficiencies in AI-driven workflows.
You’d build a tool that evaluates LLM outputs using external validation methods, such as cross-referencing with databases or logical consistency checks.
Paid customers would be AI developers and enterprises integrating LLMs into critical systems.
The MVP could focus on validating factual claims by cross-referencing with curated datasets.
The biggest risk is keeping up with rapidly evolving LLM capabilities, so focus on modular design.