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While OpenAI API has a lot of capabilities on its own, it is limited to 2000 characters per prompt. This puts a need to be able to determine optimal types of prompt selection and examples included in the prompt for complex queries. Rather than try to do everything at once, subtasks can be delegated to different API calls with subtask-optimized prompts.

I don't know if there's a better term for this, so I'm tentatively calling this behavior chaining and can change this if it turns out there's a better name.

Chaining ends up boiling down into a couple different tasks. First, you need to figure out what a prompt is actually asking for. To do this you can decompose the task into its component parts and figure out the sort of prompts you need to query to answer the parts

For each of those sub-parts, you need to be able to figure out how to optimize the prompt so that it optimizes the answer via context stuffing

However, during all of this, you want to make sure that you're ensuring that the API is in compliance with safety practices for your task

Add a new chaining type
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