As organizations move away from "vibes-based" development, the ability to quantify model reliability, safety, and cost has become the most sought-after skill in AI engineering. This comprehensive bootcamp from the School of AI is designed to provide you with the technical authority to lead this transition. You will move beyond simple prompt engineering to master the full lifecycle of LLM evaluation, from designing high-signal annotation taxonomies to implementing automated "LLM-as-a-judge" workflows. By focusing on architecture-specific metrics like RAG Faithfulness and Agentic Tool Selection Accuracy, you gain the ability to build self-correcting systems that maintain quality at scale. This curriculum provides the rigorous blueprint needed to architect AI systems that are not just impressive, but demonstrably reliable and cost-optimized for production environments.

This Course Offers

• Architecture-Specific Benchmarking: You will be able to design specialized test suites for Retrieval-Augmented Generation (RAG) using metrics like Contextual Precision and Answer Relevancy.
• Automated Evaluation Pipelines: Master the integration of tools like LangSmith, DeepEval, or Giskard into your CI/CD gates to prevent performance regressions before they reach your users.
• Failure Mode Taxonomy: Learn to systematically identify and categorize model errors—from subtle hallucinations and "confidently incorrect" JSON to complex multi-step reasoning collapses.
• Cost & Performance Optimization: Gain the skills to implement intelligent routing and semantic caching, ensuring your high-reasoning models (like GPT-4o or Claude 3.5) are used only when necessary.

Why We Love This Course

1. Production-First Mentality: It is clear that this course values operational reality, treating evaluation not as a one-time test but as a continuous observability loop.
2. Strategic Cost Management: You can tell the curriculum prioritizes ROI, teaching you how to build fallback logic and caching layers that can reduce API spend by up to 60%.
3. Human-Centered Reliability: The approach feels incredibly practical, focusing on how to calibrate automated judges against human expert reviews to ensure your metrics actually reflect user satisfaction.
4. Scalable Engineering Patterns: What sets it apart is the focus on "Evaluation-Driven Development" (EDD), a methodology that uses test datasets to guide prompt and model iteration.

The digital era no longer rewards those who simply "use" AI; it rewards those who can guarantee its performance. The question is whether you will continue to deploy models blindly or become the architect who can prove their safety, accuracy, and efficiency. This bootcamp provides the technical framework and analytical rigor to own the LLM evaluation pipeline. Start building reliable AI systems today.

• AI/ML engineers building or fine-tuning LLM applications and workflows
• Product managers responsible for the performance, safety, and business impact of AI features
• MLOps and infrastructure teams looking to implement evaluation pipelines and monitoring systems
• Data scientists and analysts who need to conduct systematic error analysis or human-in-the-loop evaluation
• Technical founders, consultants, or AI leads managing LLM deployments across organizations
• Anyone curious about LLM performance evaluation, cost optimization, or risk mitigation in real-world AI systems

• No prior experience in evaluation required—this course starts with the fundamentals
• Basic understanding of how large language models (LLMs) like GPT-4 or Claude work
• Familiarity with prompt engineering or using AI APIs is helpful, but not required
• Comfort reading JSON or working with simple scripts (Python or notebooks) is a plus
• Access to a computer with internet connection (for labs and dashboards)
• Curiosity about building safe, measurable, and cost-effective AI systems!