APE — автоматическая оптимизация промптов

# Simplified APE implementation
def ape_optimize(task_description, examples, num_candidates=10):
    # Step 1: Generate prompt candidates
    candidates = llm.generate(f"""
        Generate {num_candidates} different prompts for this task:
        Task: {task_description}

        Each prompt should be a complete instruction that could be
        used to solve this task. Be creative and diverse.
    """)

    # Step 2: Evaluate each candidate
    scores = []
    for prompt in candidates:
        correct = 0
        for inp, expected in examples:
            result = llm.generate(f"{prompt}\n\nInput: {inp}")
            if result.strip() == expected:
                correct += 1
        scores.append(correct / len(examples))

    # Step 3: Return best prompt
    best_idx = scores.index(max(scores))
    return candidates[best_idx], scores[best_idx]