def make_simulation_prompt(self, tokens: Sequence[str]) -> str | list[ChatMessage]:
        """Create a few-shot prompt for predicting neuron activations for the given tokens."""

        # TODO(sbills): The prompts in this file are subtly different from the ones in explainer.py.
        # Consider reconciling them.
        prompt_builder = PromptBuilder()
        prompt_builder.add_message(
            Role.SYSTEM,
            """We're studying neurons in a neural network.
Each neuron looks for some particular thing in a short document.
Look at summary of what the neuron does, and try to predict how it will fire on each token.

The activation format is token<tab>activation, activations go from 0 to 10, "unknown" indicates an unknown activation. Most activations will be 0.
""",
        )

        few_shot_examples = self.few_shot_example_set.get_examples()
        for i, example in enumerate(few_shot_examples):
            prompt_builder.add_message(
                Role.USER,
                f"\n\nNeuron {i + 1}\nExplanation of neuron {i + 1} behavior: {EXPLANATION_PREFIX} "
                f"{example.explanation}",
            )
            formatted_activation_records = format_activation_records(
                example.activation_records,
                calculate_max_activation(example.activation_records),
                start_indices=example.first_revealed_activation_indices,
            )
            prompt_builder.add_message(
                Role.ASSISTANT, f"\nActivations: {formatted_activation_records}\n"
            )

        prompt_builder.add_message(
            Role.USER,
            f"\n\nNeuron {len(few_shot_examples) + 1}\nExplanation of neuron "
            f"{len(few_shot_examples) + 1} behavior: {EXPLANATION_PREFIX} "
            f"{self.explanation.strip()}",
        )
        prompt_builder.add_message(
            Role.ASSISTANT, f"\nActivations: {format_sequences_for_simulation([tokens])}"
        )
        return prompt_builder.build(self.prompt_format)
Create a few-shot prompt for predicting neuron activations for the given tokens.
neuron_explainer/explanations/simulator.py