What You'll Learn 

• Explain the concept of Test-Time Compute and its role in improving model performance during inference without additional training or fine-tuning.
• Apply Chain-of-Thought prompting to enable step-by-step reasoning in large language models, making their problem-solving process transparent and more reliable.
• Use Best-of-N sampling to generate multiple candidate outputs and select the most accurate one through systematic evaluation and comparison.
• Implement self-verification strategies to allow models to check and refine their own answers, creating feedback loops that improve consistency.
• Analyze how increasing test-time compute affects solution accuracy, consistency, and efficiency, learning when these techniques provide meaningful improvements versus diminishing returns.

Who Should Enroll

• ML engineers and AI practitioners who want to optimize model performance at inference time and understand the reasoning strategies behind recent AI breakthroughs.
• Developers working with LLMs who need practical techniques to improve output quality and reliability without retraining or fine-tuning models.
• Data scientists interested in exploring how computational budgets at inference time can be strategically allocated to enhance reasoning and problem-solving capabilities.

Why Enroll

What You'll Need