Stop Guessing Which LLM to Use. Learn the Metrics That Decide It. AI engineers and data scientists who can evaluate large language models objectively — not just use them — are the professionals organizations trust with critical AI decisions. This intermediate-level Learning Kit teaches you to assess any LLM using industry-standard metrics like BLEU, ROUGE, F1, and HELM, and to navigate the real trade-offs between accuracy, latency, cost, model size, and ethical considerations. Six focused courses give you a framework you can apply immediately to LLM selection, deployment, and optimization decisions. What is included in this Learning Kit 6 courses, 10+ hours of Skillsoft video training covering LLM evaluation metrics, performance trade-offs, cost analysis, and ethical AI considerations Expert tutor support available 24/7, so you get answers when you are stuck, not when it is convenient for someone else 365-day access with no subscription and no expiry pressure Organizations looking to upskill AI teams at scale can explore our corporate volume solutions. Why this beats piecing it together from blog posts LLM evaluation is covered in scattered blog posts, academic papers, and vendor marketing, none of it structured for practical decision-making. This kit gives you a single, coherent framework covering every dimension that matters: quality metrics, latency, cloud cost, model size, and bias. You get structured Skillsoft content with 365-day access for the price of a few hours of consultant time, with tutor support available around the clock if you need to go deeper. This Learning Kit is built for you if You build or deploy LLM-powered applications and need to compare model options objectively You are responsible for AI infrastructure costs and want to understand the size-versus-performance trade-off Your organization is evaluating in-house LLMs versus public APIs and needs a structured decision framework You work in data science or MLOps and want to add formal LLM evaluation skills to your profile You need to address bias, fairness, and ethical considerations in your AI systems Roles that benefit from this training AI Engineer Machine Learning Engineer Data Scientist MLOps Engineer AI Product Manager What does this training cover LLM Evaluation Metrics: BLEU, ROUGE, F1, and HELM + Build a solid foundation in the metrics used to evaluate large language models at scale. You will learn to distinguish between intrinsic and extrinsic evaluation methods, apply BLEU and ROUGE for text generation tasks, use F1 scoring for classification, and interpret HELM benchmark results across multiple evaluation dimensions. Accuracy and Performance Assessment + Learn how to measure and compare LLM accuracy across different task types and use cases. This section covers performance benchmarking approaches, how to interpret accuracy scores in context, and the relationship between model training data quality and downstream task performance. Latency, Throughput, and Scalability Trade-Offs + Latency and throughput directly affect user experience and infrastructure costs. You will learn how to measure and manage these factors in real-world LLM deployments, explore the relationship between model size and response time, and understand scalability constraints when moving from prototype to production workloads. Cost Evaluation and Cloud Deployment Economics + Evaluate the computational and financial costs of deploying small, medium, and large language models on cloud platforms including AWS and Azure. This section covers resource optimization strategies, how to compare in-house versus public LLM costs under different operational constraints, and how to align model selection with budget requirements. Model Size and In-House vs. Public LLM Trade-Offs + Larger models generally offer better performance on complex tasks, but at a significantly higher resource cost. You will learn to evaluate the trade-offs between model size, accuracy, and resource consumption, and develop a framework for deciding when a smaller fine-tuned model outperforms a larger general-purpose one for your specific use case. Bias, Fairness, and Ethical Considerations + Address the ethical dimensions of LLM evaluation that go beyond performance numbers. This section covers how to identify and measure bias in model outputs, approaches to fairness assessment, and practical strategies for optimizing model performance while maintaining ethical standards and reducing harmful outputs. Where can this training take your career Career paths and next steps after LLM Metrics and Trade-Offs training + LLM evaluation skills are becoming a required competency for AI roles as organizations move from AI experimentation to production deployment. After completing this kit, many professionals expand into the full LLM lifecycle with Natural Language Processing and LLMs, build production pipelines with MLOps, or extend into API-level model integration with Leveraging Generative AI APIs. For a complete AI training overview, explore our Artificial Intelligence training collection. Frequently Asked Questions Do I need coding experience or a math background to take this training + This kit is designed for intermediate-level AI practitioners, not beginners. You should have a working understanding of machine learning concepts and some exposure to AI or NLP workflows before starting. While the training does not require advanced mathematics, familiarity with model evaluation concepts will help you apply the content more effectively. Coding experience is useful but not required to follow the evaluation frameworks taught. What is the difference between BLEU, ROUGE, HELM, and F1 in LLM evaluation + These metrics measure different aspects of model performance. BLEU and ROUGE are n-gram overlap metrics used primarily for text generation and summarization tasks. F1 is a precision-recall balance metric commonly used for classification and information extraction. HELM is a holistic benchmark framework that evaluates LLMs across multiple dimensions simultaneously, including accuracy, robustness, fairness, and efficiency. This training teaches you when to use each metric and how to interpret the results in context. What do LLM trade-offs mean in practice and why do they matter + In practice, trade-offs mean that improving one dimension of an LLM's performance often degrades another. A larger model may score higher on accuracy benchmarks but cost significantly more to run and introduce higher latency in production. A smaller fine-tuned model may be faster and cheaper but underperform on out-of-domain inputs. This training gives you a structured way to quantify these trade-offs and make defensible decisions based on your specific use case and infrastructure constraints. Is the exam voucher included and how do I register for the exam + The exam voucher is not included in this training. The exam is administered globally by Pearson VUE, either at an authorized testing center or via online proctoring. Once your preparation is complete, you register and purchase your exam voucher directly through the official certification or Pearson VUE website. Can my team or organization get certified together + Yes. DiviTrain offers volume licensing for teams and organizations looking to upskill at scale. Whether you are upskilling a small AI team or rolling out training across departments, our corporate solutions provide flexible access and invoicing options. Visit our For Teams page to learn more.