Let's say you want an LLM to generate product descriptions. You might have a first pass where you generate a handful (each run will be slightly different). Th

Let's say you want an LLM to generate product descriptions. You might have a first pass where you generate a handful (each run will be slightly different). Then you can throw those against a pass where you give a system message of an in market shopper matching your target demo asking for the points that are most relevant to making a decision. Then you give the descriptions and bullet points to a final pass with a system message as an expert copywriter summarizing the multiple descriptions with an emphasis on the bullet points. You'll have MUCH better results than anything you'd be able to generate in a single pass. You'll need to play with zero shot vs few shot in each step, and there's other little hacks for consistency here and there that would be relevant, but a multipass approach like this makes a world of difference. As for code, it'd certainly be worth a try of taking the output from pass 1 and then running it though another pass "Can you refactor this code with an emphasis on reducing cyclomatic completely while improving readability and its future maintenance?" Though in general I'd love to see a dedicated refactoring fine tuned layer (or language specific layers) as an offering in the next year or so, as it's a task nuanced enough I don't know that a general purpose model is going to be great. But yes, I could definitely see running initial code generations though a second pass for improving the results.