> A workflow has hardcoded branching paths; explicit if conditions and instructions on how to behave if true.
That is very much true of the systems most of us have built.
But you do not have to do this with an LLM; in fact, the LLM may decide it will not follow your explicit conditions and instructions regardless of how hard you you try.
That is why LLMs are used to review the output of LLMs to ensure they follow the core goals you originally gave them.
For example, you might ask an LLM to lay out how to cook a dish. Then use a second LLM to review if the first LLM followed the goals.
This is one of the things tools like DSPy try to do: you remove the prompt and instead predicate things with high-level concepts like "input" and "output" and then reward/scoring functions (which might be a mix of LLM and human-coded functions) that assess if the output is correct given that input.
That is very much true of the systems most of us have built.
But you do not have to do this with an LLM; in fact, the LLM may decide it will not follow your explicit conditions and instructions regardless of how hard you you try.
That is why LLMs are used to review the output of LLMs to ensure they follow the core goals you originally gave them.
For example, you might ask an LLM to lay out how to cook a dish. Then use a second LLM to review if the first LLM followed the goals.
This is one of the things tools like DSPy try to do: you remove the prompt and instead predicate things with high-level concepts like "input" and "output" and then reward/scoring functions (which might be a mix of LLM and human-coded functions) that assess if the output is correct given that input.