audience_analysis = {
    'ceo': {
        'care_about': ['Business impact', 'Revenue', 'Competitive advantage', 'Risk'],
        'dont_care_about': ['Algorithm details', 'Technical implementation', 'Code'],
        'time': 'Very limited — get to the point fast',
        'analogies': 'Business, sports, everyday life',
    },
    'product_manager': {
        'care_about': ['User impact', 'Feature performance', 'Next steps'],
        'dont_care_about': ['Model architecture', 'Training process'],
        'time': 'Moderate — provide context',
        'analogies': 'User behavior, product metrics',
    },
    'engineer': {
        'care_about': ['Technical details', 'Implementation', 'Scalability'],
        'dont_care_about': ['Business context', 'High-level summaries'],
        'time': 'Patient — ready for deep dives',
        'analogies': 'Technical concepts, systems design',
    },
}

eli5_technique = {
    'step_1': 'Start with the "what" — what does it do?',
    'step_2': 'Explain the "why" — why does it matter?',
    'step_3': 'Use an analogy — compare to something familiar',
    'step_4': 'Show the impact — what does it change?',
    'step_5': 'End with the "so what" — what should we do?',
}

analogy_toolkit = {
    'machine_learning': {
        'analogy': 'A student learning from practice exams',
        'explanation': 'The model studies thousands of examples, learns the patterns, and then takes a test on new examples it hasn\'t seen before.',
    },
    'neural_network': {
        'analogy': 'Layers of filters in an image',
        'explanation': 'Like looking through stacked filters — each layer picks up different features, from edges to shapes to objects.',
    },
    'training_data': {
        'analogy': 'Textbooks for a student',
        'explanation': 'The quality and variety of training data determines how well the model learns. Bad textbooks lead to bad learning.',
    },
    'overfitting': {
        'analogy': 'Memorizing answers instead of understanding concepts',
        'explanation': 'A student who memorizes practice exams but fails the real test — the model learned the training data too well and can\'t generalize.',
    },
    'a_b_testing': {
        'analogy': 'Taste-testing two recipes',
        'explanation': 'We give half our users recipe A and half recipe B, then see which one people prefer. Simple, scientific, definitive.',
    },
    'feature_engineering': {
        'analogy': 'Choosing what to measure when evaluating a car',
        'explanation': 'Do you measure horsepower? Fuel efficiency? Safety rating? The features you choose determine how well you can predict car quality.',
    },
    'recommendation_system': {
        'analogy': 'A great DJ at a party',
        'explanation': 'A DJ reads the crowd, learns what they like, and plays songs that keep the energy up. Our system does the same for content.',
    },
    'natural_language_processing': {
        'analogy': 'Teaching a computer to read',
        'explanation': 'Like teaching a child to read — first letters, then words, then sentences, then meaning. The computer learns to understand human language.',
    },
}

communication_do = {
    'start_with_why': 'Explain why this matters before how it works',
    'use_analogies': 'Compare to familiar concepts',
    'show_impact': 'Connect to business outcomes',
    'be_confident': 'Own your expertise',
    'invite_questions': 'Show you\'re open to dialogue',
    'end_with_action': 'Tell them what to do next',
}

communication_dont = {
    'no_jargon': 'Avoid terms like "gradient descent," "backpropagation," "epochs"',
    'no_apologizing': 'Don\'t say "this is complicated" — it undermines confidence',
    'no_rushing': 'Take your time to be clear',
    'no_hiding_uncertainty': 'Be honest about limitations',
    'no_information_overload': 'Stick to 3 key points',
    'no_monologuing': 'Make it a conversation',
}

handling_accuracy_question = {
    'bad_answer': '95.3% accuracy',
    'good_answer': 'Out of 100 fraud cases, we catch 93 of them. The 7 we miss are typically very sophisticated attacks that look like normal transactions. Meanwhile, we flag about 5 legitimate transactions per 1000 as suspicious — a low false alarm rate that keeps our customers happy.',
    'why_better': 'Translates abstract metric into concrete, understandable terms',
}

handling_trust_question = {
    'bad_answer': 'The math checks out',
    'good_answer': 'Three reasons: (1) We test it against historical fraud we know about — it catches 93%. (2) We run it alongside human experts who validate its decisions. (3) We monitor it continuously — if it starts making mistakes, we catch it within hours.',
    'why_better': 'Provides concrete evidence and shows ongoing vigilance',
}

handling_risk_question = {
    'bad_answer': 'No system is perfect',
    'good_answer': 'The main risks are: (1) New fraud patterns it hasn\'t seen before — we address this by retraining monthly. (2) False positives that annoy customers — we have a feedback loop to reduce these. (3) Bias against certain customer groups — we test for fairness regularly.',
    'why_better': 'Acknowledges real risks and shows mitigation strategies',
}