LLM Evaluation: Building Reliable AI Systems at Scale
LLM evaluation has become critical as organisations deploy large language models in production environments. This comprehensive course from Educative teaches professionals how to build robust testing frameworks for AI systems at scale. You’ll master trace capture techniques, synthetic data generation, and evaluation methodologies specifically designed for agent-based systems and retrieval-augmented generation (RAG) architectures. The curriculum covers production-ready testing workflows that ensure LLM applications maintain reliability and performance as they scale. Through interactive exercises, you’ll develop practical skills in monitoring model behaviour, detecting performance degradation, and implementing automated evaluation pipelines that catch issues before they impact users.
Course Snapshot
| Provider | Educative |
| Price | Subscription |
| Duration | Self-paced |
| Difficulty | Advanced |
| Format | Interactive, browser-based (no setup needed) |
| Certificate | Yes, on completion |
| Last Verified | February 2026 |
What This Generative AI Course Covers
The course delivers in-depth training on essential LLM evaluation techniques including distributed tracing systems for capturing model interactions, synthetic dataset creation for comprehensive testing scenarios, and specialised evaluation frameworks for agentic AI systems. You’ll work with retrieval-augmented generation evaluation methodologies, learning to assess both retrieval accuracy and generation quality. The curriculum covers performance monitoring tools, bias detection techniques, and automated evaluation pipelines that integrate with modern MLOps workflows.
Learning occurs through Educative’s interactive browser-based platform featuring hands-on coding exercises and real-world scenario simulations. You’ll build actual evaluation systems, implement trace collection mechanisms, and create testing workflows using industry-standard tools. Interactive labs guide you through synthetic data generation techniques, whilst practical projects involve designing comprehensive test suites for different LLM architectures. The course emphasises experiential learning through building production-ready evaluation infrastructure.
These skills directly address current industry challenges in AI system reliability and governance. Professionals gain expertise essential for MLOps roles, AI safety compliance, and production LLM deployment in enterprise environments. The curriculum draws on principles of large language model, applied to real-world scenarios.
Who Should Take This Generative AI Course
About Educative
Educative is a browser-based learning platform specialising in software engineering and system design. Unlike video-based platforms, Educative uses interactive text-based lessons with embedded coding environments, so you can practise directly without setting up a local development environment.
Frequently Asked Questions
How long does LLM Evaluation: Building Reliable AI Systems at Scale take to complete?
The course is self-paced, typically requiring 15-20 hours depending on your experience with LLM systems and evaluation frameworks.
What career opportunities does this course support?
Graduates are well-positioned for MLOps engineer, AI safety specialist, and senior ML engineer roles focusing on production AI systems.
What prerequisites are needed for this course?
Solid Python programming skills and familiarity with machine learning concepts are essential. Prior LLM experience is helpful but not mandatory.
How does this course address AI safety and governance requirements?
The evaluation techniques align with emerging AI governance frameworks, including those outlined by the UK AI Safety Institute for responsible AI deployment. For further reading, see UK AI Safety Institute.
Master Production LLM Evaluation Today
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