Overview

Building Reliable LLM Systems is a comprehensive course for AI practitioners looking to move beyond basic models and create production-grade applications. While getting an LLM to generate text is easy, ensuring a consistently accurate, relevant, and trustworthy output is a significant engineering challenge. This course provides a systematic framework for tackling the entire lifecycle of LLM reliability. You will start by learning to quantitatively evaluate model performance using a suite of lexical and semantic metrics, such as BLEU, ROUGE-L, and cosine similarity. You’ll dive deep into debugging, using log analysis and data manipulation to uncover the root causes of critical failures, such as hallucinations, by correlating them with retrieval system performance. The course emphasizes statistical rigor, teaching you to design and analyze A/B tests, apply hypothesis testing, and calculate confidence intervals to prove the significance of your optimizations. Finally, you’ll optimize the foundational data layers, learning to tune SQL queries and vector search parameters to achieve the perfect balance between recall and latency.

Syllabus

Evaluate and Optimize LLM Performance

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.

Analyze Logs: Fix LLM Hallucinations

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.

Evaluate LLMs: Test and Prove Significance

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.

Optimize SQL and Vector Search Parameters

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.

End-to-End LLM Performance Audit

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.