Wherever possible, the chatbot output is deterministic, in that to answer a query, we're realtime generating and running code or SQL against your data. Our LLM orchestrates that, and finally evaluates whether the output correctly and adequately answers the question.
We also extensively use synthetic data and examples to guide and constrain our models.
Another way we're ensuring good-quality output is to ensure good-quality _input_ -- by enriching the detail and specificity of the user's question, and asking the user to disambiguate when we determine the question is too broad.
Wherever possible, the chatbot output is deterministic, in that to answer a query, we're realtime generating and running code or SQL against your data. Our LLM orchestrates that, and finally evaluates whether the output correctly and adequately answers the question.
We also extensively use synthetic data and examples to guide and constrain our models.
Another way we're ensuring good-quality output is to ensure good-quality _input_ -- by enriching the detail and specificity of the user's question, and asking the user to disambiguate when we determine the question is too broad.