LLM Evaluation: DeepSeek, Phi, and Llama with Chain of Thought

Evaluating the true capabilities and reasoning limits of modern language models requires more than standard surface-level testing. This course guides you through structured methods to benchmark and analyze models such as DeepSeek, Phi, and Llama using advanced evaluation strategies like Chain-of-Thought reasoning. You will gain the practical skills needed to design robust test prompts, measure multi-step reasoning performance, and compare model outputs for real-world tasks. What you'll learn: - Understand foundational metrics and scoring frameworks for large language model evaluation - Apply Chain-of-Thought prompting patterns to assess complex problem-solving and reasoning - Compare architectural strengths across DeepSeek, Phi, and Llama models - Measure accuracy, consistency, and hallucination rates across structured benchmarks - Design reproducible test datasets for domain-specific model evaluation - Analyze reasoning efficiency, token usage, and latency trade-offs The course begins with fundamental terminology and core benchmarking concepts before progressing to detailed prompt engineering for evaluation and cross-model comparison workflows. Designed for developers, data analysts, and AI enthusiasts looking to assess modern language models systematically. No advanced prior machine learning background is required. Start reading today to build rigorous evaluation pipelines for modern open models.