Building Reliable LLM Systems

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

• Build scripts using lexical and semantic metrics to evaluate LLMs and diagnose hallucinations
• Apply hypothesis testing and confidence intervals to assess accuracy
• Utilize parameterized SQL and data manipulation for user log segmentation and data retrieval
• Analyze performance gaps to prioritize fixes and ensure production reliability

• Basic familiarity with the topic and common terminology
• Readiness to practice through applied exercises or case-based work