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AI / Apache Burr Interview questions

What is the difference between MapStates and MapActions in Burr’s parallelism API?

Both are higher-level parallelism helpers that expand into many sub-applications and then join the results, but they vary a different axis:

ClassVariesTypical use
MapStatesSame action, many states, via .states()One LLM, many prompts
MapActionsSame state, many actions, via .actions()Same prompt, many different LLMs

class TestMultiplePrompts(MapStates):
    def action(self, state, inputs):
        return query_llm.with_name("query_llm")
    def states(self, state, context, inputs):
        for prompt in [...]:
            yield state.update(prompt=prompt)
    def reduce(self, state, states):
        return state.update(all_llm_outputs=[s["llm_output"] for s in states])

Both must implement .reads(), .writes(), and .reduce() in addition to their differing method (.states() vs .actions() plus its companion .state()/.action() for the fixed side).

To run the same LLM prompt across five different models in parallel, you would use:
To run one fixed LLM against ten different prompts in parallel, you would use:

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