This module lays the groundwork for quantitative Large Language Mode (LLM) evaluation. Learners will discover why relying on intuition to judge model performance is unsustainable and explore the foundational metrics used to create automated, objective evaluation systems. We will cover both lexical similarity metrics (like BLEU and ROUGE-L) that assess text structure and semantic metrics (like cosine similarity) that capture meaning. By the end of this module, learners will have the conceptual understanding and practical code to build their first automated evaluation script.

When a production chatbot starts giving incorrect answers, how do you find the problem and fix it? This module equips AI practitioners, ML engineers, and data analysts with the essential skills for debugging production LLMs. Go beyond theory and learn the systematic, data-driven workflow that professionals use to solve the critical problem of AI hallucinations. You will be equipped to transition from merely observing AI failures to expertly diagnosing and resolving them.

When making high-stakes deployment decisions, a simple accuracy score is not enough. This module equips you with the statistical methods to rigorously validate LLM performance improvements. By the end of this module, you will be able to move beyond subjective "it seems better" evaluations to confidently state, "we can prove it's better," ensuring every deployment decision is backed by sound statistical evidence.

In the world of large-scale AI, slow queries and inefficient search can bring a system to its knees. This module provides the critical skills to prevent that, focusing on practical database and vector search optimization techniques. By the end of this module, you will be equipped to systematically analyze and optimize production retrieval systems, ensuring your AI applications are not only powerful but also fast and reliable.

In this module, you will conduct an end-to-end performance audit comparing two LLM variants using an A/B test dataset. You will implement a pipeline to calculate key performance metrics, including lexical and semantic similarity, and use statistical A/B testing to validate model improvements. The project culminates in a comprehensive report where you will correlate hallucination rates with retrieval logs and synthesize your findings into data-driven recommendations for stakeholders, guiding the decision for a production-level rollout in a customer support application.