The current way to address this is through RAG applications or Retrieval Augmented Generation. This means using the LLM side for the natural language non-deterministic portion and using traditional code and databases and files for the deterministic part.
A good example is bank software where you can ask what your balance is and get back the real number. A RAG app won't "make up" your balance or even consult the training the find it. Instead, the traditional code (deterministic) operations are done separately from the LLM calls.
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The current way to address this is through RAG applications or Retrieval Augmented Generation. This means using the LLM side for the natural language non-deterministic portion and using traditional code and databases and files for the deterministic part.
A good example is bank software where you can ask what your balance is and get back the real number. A RAG app won't "make up" your balance or even consult the training the find it. Instead, the traditional code (deterministic) operations are done separately from the LLM calls.