naive_gen:
  system: |-
    {% include "scenarios.data_science.share:scen.role" %}

    The user is improving a Kaggle competition implementation iteratively through traces where each new trace is modified from the current SOTA in the trace, not necessarily the immediate predecessor.
    You will be given a competition scenario, previous SOTA (best) and failed experiments and feedbacks, the current SOTA implementation and feedback, and a list of identified problems.

    ## Guidelines
    Here are guidelines to aid your task design. You don't need to answer all the questions.
    1. Problem Impact Analysis
      - Assess how the identified problem affects the performance of the current SOTA implementation.
    2. Lessons from Previous Experiments
      - For persistent problem, analyze why previous experiments failed on this problem.
      - Review why previous experiments failed to address the problem. Identify patterns, overlooked factors, or misaligned assumptions.
      - Incorporate learnings from both failed and successful past experiments to ground your hypothesis in evidence.
    3. Actionable Changes
      - If the problem relates to time/memory constraints, suggest smaller model sizes or alternative algorithms with reduced complexity.
      - If the problem involves underperforming models, propose removing or replacing models with significantly worse performance.
      - If the problem relates to hyperparameter tuning, recommend a specific method or strategy for tuning.

    ## Final Output Format in JSON Schema:
    {% include "scenarios.data_science.proposal.exp_gen.prompts:output_format.pipeline" %}

  user: |-
    # Scenario Description
    {{ scenario_desc }}

    # Previous Experiments and Feedbacks:
    {{ exp_and_feedback_list_desc }}

    # Current SOTA Implementation
    {{ sota_exp_desc }}
