Mastering LLM Evaluation: learn how to test RAG , Agentic AI using Ragas, DeepEval, LangSmith. Learn how to test GenAI.
β±οΈ Length: 2.8 total hours
β 4.41/5 rating
π₯ 201 students
π August 2025 update
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- Strategic Evaluation Frameworks: Master conceptual frameworks for comprehensively assessing generative AI, ensuring performance, safety, and reliability across diverse applications.
- Debugging AI Black Boxes: Acquire advanced diagnostic skills to systematically uncover failure patterns in LLM-powered systems and agentic workflows, translating errors into actionable insights.
- Architecting Quality Assurance for AI: Learn to embed robust, automated testing methodologies directly into AI development pipelines for continuous quality from build to deployment.
- Driving User Value with Metrics: Understand how tailored evaluation metrics directly correlate with user satisfaction, guiding data-driven decisions for model refinement and feature enhancement.
- Cultivating Trustworthy AI: Develop expertise to rigorously validate AI outputs for accuracy, coherence, and ethical considerations, building confidence in RAG and agentic systems.
- Proactive Problem Anticipation: Cultivate the ability to foresee and mitigate potential issues in Gen AI responses, designing sophisticated test scenarios to uncover edge-case failures.
- Implementing Continuous Improvement Loops: Master dynamic, automated feedback mechanisms that channel evaluation results into prompt engineering and model fine-tuning for agile development.
- Navigating the AI Evaluation Landscape: Gain perspective on the rapidly evolving ecosystem of AI evaluation tools, strategically selecting and integrating suitable technologies for project needs.
- Scalable Data Generation for Rigorous Testing: Explore innovative techniques for programmatically constructing high-fidelity, diverse, and challenging evaluation datasets representing real-world scenarios.
- Benchmarking AI Performance: Learn to scientifically compare foundational models, RAG configurations, and agentic strategies to identify optimal approaches for specific business objectives.
- Ensuring AI Robustness and Resilience: Discover how to design tests that challenge the stability and consistency of AI systems under varying conditions, building applications that perform reliably.
- Strategic Error Taxonomy and Remediation: Develop a systematic approach to categorizing observed AI failures, enabling targeted remediation strategies that address root causes for more resilient applications.
- PROS:
- Highly Practical and Tool-Focused: Direct engagement with industry-standard tools like Ragas, DeepEval, and LangSmith provides immediate, applicable skills.
- Addresses a Critical Skill Gap: Fills a significant industry need for specialists capable of validating complex AI systems, enhancing career prospects in a booming field.
- Emphasis on Automation: Teaches how to build automated testing pipelines using Python and Pytest, crucial for scalable and efficient AI development.
- Comprehensive Scope: Covers evaluation techniques across various AI components (LLM, RAG, Agentic AI), offering a holistic understanding of Gen AI quality assurance.
- CONS:
- Limited Depth Due to Short Duration: Given the extensive range of topics and tools covered, the 2.8-hour total duration might only offer a high-level overview, potentially requiring further self-study for mastery.
Learning Tracks: English,IT & Software,Other IT & Software
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