AI Evals For Engineers & PMs Course
If you’ve ever built an AI feature and thought, “I hope this still works…” — you’re not alone.
Most engineers and product managers today are shipping AI products without a real way to measure what’s actually happening under the hood. Outputs look okay… until they don’t. A prompt tweak improves one thing and silently breaks another.
AI Evals For Engineers & PMs is the course that fixes that problem.
Instead of guessing, you’ll learn how to build evaluation systems that give you clear answers. You’ll understand what’s working, what’s broken, and exactly where to focus your time.
What You Get
Inside the course, you’re not just getting theory. You’re getting a complete system you can apply immediately.
- Full access to all lessons and materials anytime
- Recorded sessions + live-style explanations
- Access to future updates and new cohorts
- Private community where you can ask questions and get unstuck
- Office hours and real interaction with experts
- Detailed course notes (150+ pages)
- Hands-on assignments to actually practice what you learn
What You’ll Actually Learn
This isn’t one of those courses that just explains concepts and leaves you confused. Everything is built around real problems you’re probably already facing.
1. How to Test AI Properly
You’ll learn how to evaluate outputs even when they’re subjective — which is one of the hardest parts of working with AI.
No more guessing if your model “feels better.” You’ll know.
2. How to Collect (or Create) the Right Data
No users? No problem.
You’ll discover how to generate synthetic data and still build strong evaluation systems from day one.
3. How to Find What’s Actually Broken
Instead of randomly debugging, you’ll learn how to quickly identify patterns, errors, and weak points in your AI system.
This alone can save you weeks of wasted time.
4. How to Build Evals That Make Sense
Most people use generic evaluations that don’t really help.
You’ll learn how to create evals tailored to your product — the kind that actually give you useful insights.
5. How to Run Everything in Production
This is where things get real.
You’ll integrate evaluation into your workflow, automate testing, and make sure every update improves your system instead of breaking it.
AI Evals For Engineers & PMs – No.1 Course at Maven Modules
Lesson 1: Fundamentals & Lifecycle of LLM Evaluation
You start by understanding the foundation — why evaluation is not optional, but critical.
- Why evaluation matters (business impact + risk reduction)
- Common failure modes in LLM applications
- The full lifecycle: from development → production
- Instrumentation & observability basics
- Introduction to structured error analysis
Lesson 2: Systematic Error Analysis
This is where things get practical. You’ll learn how to actually find what’s broken.
- Generating synthetic data to test your system
- Annotating and analyzing qualitative outputs
- Turning errors into clear improvement actions
- Avoiding common analysis mistakes
- Practical: Build your own error tracking system
Lesson 3: Implementing Effective Evaluations
Now you move from analysis to building real evaluation systems.
- Defining metrics with code-based and LLM-as-a-judge methods
- Evaluating individual outputs and full system performance
- Structuring datasets for reliable results
- Practical: Build an automated evaluation pipeline
Lesson 4: Collaborative Evaluation Practices
Evaluation isn’t just technical — it’s also about team alignment.
- Designing team-based evaluation workflows
- Measuring inter-annotator agreement
- Building shared evaluation standards
- Practical: Collaborative evaluation exercise
Lesson 5: Architecture-Specific Evaluation Strategies
Different AI systems require different evaluation approaches.
- Evaluating RAG systems (retrieval relevance + factual accuracy)
- Testing multi-step pipelines and error propagation
- Evaluating tool usage and multi-turn conversations
- Handling multimodal AI (text, image, audio)
- Practical: Build targeted test suites
Lesson 6: Production Monitoring & Continuous Evaluation
This is where your system becomes real and scalable.
- Tracking behavior with traces, spans, and sessions
- Automating evaluations inside CI/CD pipelines
- Comparing experiments consistently
- Setting up safety and quality guardrails
- Practical: Build a monitoring dashboard
Lesson 7: Continuous Human Review Systems
Automation alone isn’t enough — human feedback still matters.
- Strategic sampling for efficient reviews
- Designing better review interfaces
- Building continuous feedback loops
- Practical: Create a feedback system
Lesson 8: Cost Optimization
Finally, you’ll learn how to make your AI system efficient and scalable.
- Balancing performance vs cost in LLM systems
- Smart model routing based on complexity
- Reducing unnecessary API usage
Why This Course Matters
AI is powerful, but without proper evaluation, it’s unpredictable.
This course teaches you how to bring structure into that chaos. You’ll stop relying on gut feeling or random testing and start using real feedback loops that continuously improve your AI systems.
The goal is simple: build AI that you can actually trust.
Who This Course Is For
- Engineers working with LLMs, prompts, or AI systems
- Product managers responsible for AI features
- Teams tired of manually checking outputs
- Anyone building AI products without clear metrics
If you’ve ever asked yourself “Is this actually working?” — this course is for you.
The Real Value
Most AI builders focus on prompts, models, and tools.
Very few focus on evaluation — and that’s exactly why most AI products fail silently.
Once you understand evals, everything changes. You stop guessing. You move faster. You build better systems.
That’s the real advantage this course gives you.
Conclusion
If you’re serious about AI, this is one of those skills you can’t skip.
AI Evals For Engineers & PMs shows you how to build AI systems that are not just powerful — but reliable, measurable, and scalable.
Enroll AI Evals For Engineers & PMs – No.1 Course at Maven
