Working with OpenAI's models I've found a very good strategy is to have two passes if you can afford the extra tokens: one pass uses a heavy model and natural l

Working with OpenAI's models I've found a very good strategy is to have two passes if you can afford the extra tokens: one pass uses a heavy model and natural language with markdown sections discussing the reasoning and providing a final natural language answer (ideally labeled clearly with a markdown header). The second pass can use a cheaper and faster model to put the answer into a structured output format for consumption by the non-LLM parts of the pipeline. You basically use JSON schema mode to draw a clean boundary around the wishy-washy language bits, using the LLM as a preprocessor to capture its own output in a useful format.