AI Evals for Engineers and PMs Review: The Complete Guide to Evaluating and Improving AI System Performance

Why Evaluating AI Systems Is the Most Important Skill No One Is Teaching

Everyone is building with AI. But almost no one is systematically evaluating whether their AI systems actually work. When you deploy a model into production, you need to know: Is it giving correct answers? Is it biased? Is it safe? Is it getting better or worse over time? Without evaluations, you’re flying blind — and in production, that means unhappy users, legal risk, and wasted compute.

AI Evals for Engineers & PMs (from Maven) is the only course that teaches you how to build evaluation frameworks for AI systems. It covers metrics, benchmarking, red-teaming, and continuous monitoring — the skills that separate AI builders who ship reliable systems from those who ship unpredictable ones.

Inside AI Evals for Engineers and PMs

Module 1: Evaluation Fundamentals

• Why Evals Matter: The case for systematic evaluation — real-world examples of AI failures that could have been caught with proper evals.
• Types of Evaluation: The difference between automated evals, human evals, and red-teaming — and when to use each.
• Metric Design: How to design metrics that measure what you actually care about — accuracy, relevance, safety, latency, and cost.
• Ground Truth: How to create evaluation datasets that represent real-world usage — not just cherry-picked examples.

Module 2: Building Evaluation Frameworks

• Reference-Based Evals: How to evaluate AI outputs against known correct answers — classification accuracy, F1 scores, and exact match.
• Reference-Free Evals: How to evaluate AI outputs when there’s no single correct answer — coherence, relevance, helpfulness, and safety.
• LLM-as-Judge: How to use LLMs to evaluate other LLMs — prompt design, bias mitigation, and correlation with human judgment.
• Human Evaluation: How to design and run human evaluation studies — annotator training, inter-annotator agreement, and quality control.

Module 3: Red-Teaming and Safety

• Red-Teaming Fundamentals: How to systematically probe AI systems for failures, biases, and safety issues.
• Adversarial Testing: How to craft inputs designed to break the system — prompt injections, jailbreaks, and edge cases.
• Bias Evaluation: How to test for demographic bias, cultural bias, and fairness across different user groups.
• Safety Guardrails: How to implement input and output filtering, content moderation, and safety monitoring.

Module 4: Continuous Monitoring and Improvement

• Production Monitoring: How to set up dashboards and alerts that track AI system performance in real-time.
• Regression Detection: How to detect when a model update or data change has made the system worse.
• A/B Testing for AI: How to safely test model changes in production — canary deployments, shadow testing, and statistical significance.
• Feedback Loops: How to use production feedback to continuously improve your evaluation datasets and models.

What Makes This Course Different?

1. The Only Course on AI Evals. Most AI courses teach you how to build models — this is the only one that teaches you how to evaluate them.

2. For Both Engineers and PMs. Technical depth for engineers, strategic context for PMs — both roles need to understand evals.

3. Production-Focused. Every technique is designed for real-world deployment, not academic benchmarks.

Who Should Take This Course?

Perfect For:

• ML engineers who deploy AI models and need to evaluate their performance.
• Product managers who are responsible for AI feature quality and safety.
• Anyone building with LLMs who wants to ship reliable, safe AI systems.

Not For:

• People who are only interested in training models, not evaluating them.
• Anyone who doesn’t work with AI systems in production.

Final Verdict

Evaluating AI systems is the most important skill that no one is teaching — until now. AI Evals for Engineers & PMs gives you the frameworks, metrics, and practices to evaluate, monitor, and improve AI systems in production. If you’re building with AI and not systematically evaluating your systems, you’re shipping blind. This course turns the lights on.

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