AI Evals For Engineers & PMs – No.1 Course at Maven

Key Takeaways

• Stop the “Vibes” Driven Development: Learn to replace random spot-checking with systematic, data-driven evaluation pipelines.
• Master LLM-as-a-Judge: specific techniques to build automated judges that reliably grade your AI’s outputs at scale.
• Build Golden Datasets: Discover why your lack of a curated test set is the #1 reason your AI product feels fragile.
• Save Massive Amounts: This cohort-based course costs $5,000 live. We offer the complete downloadable material for a fraction of that.

Let’s be real for a second. Building a wrapper around an LLM is easy. You can do it in a weekend.

But making it reliable? That’s where the nightmares begin.

You tweak a prompt to fix one edge case, and suddenly, three other features break. You’re stuck in a game of “prompt whack-a-mole,” spot-checking a few inputs and praying everything holds together. Sound familiar?

That’s called “Vibes-Based Development,” and it’s exactly why enterprise AI projects stall in production. You need a system. You need AI Evals For Engineers & PMs.

What is AI Evals For Engineers & PMs?

AI Evals For Engineers & PMs is the industry-standard course by Hamel Husain and Shreya Shankar that teaches you how to systematically measure LLM performance. Instead of relying on intuition, it provides a rigorous engineering framework for creating “Golden Datasets,” implementing LLM-as-a-judge automation, and building CI/CD pipelines that catch regressions before your users do.

Why The “Vibes” Method is Killing Your Product

Most teams operate like this:

1. Change a prompt.
2. Run 5 random inputs.
3. Say “Looks good to me!”
4. Ship it.

Then a user types something slightly different, and the bot hallucinates or refuses to answer.

This course flips that script. Hamel and Shreya—who have basically seen every failure mode in the book—teach you that evaluation is not an afterthought; it’s the development process itself.

If you can’t measure it, you can’t improve it. Simple as that.

What You Get In The Download

At coursestodownload.com, we give you access to the full strategic playbook without the $5,000 price tag. Here is what you’ll master:

1. The Dataset “Grind” (That Everyone Skips)

Everyone wants to mess with cool tools like LangSmith or W&B. Nobody wants to sit down and curate a high-quality dataset. This course forces you to eat your vegetables. You’ll learn how to create a “Golden Dataset” that actually represents your user’s behavior.

2. LLM-as-a-Judge

Human grading is slow and expensive. You’ll learn how to prompt a stronger model (like GPT-4) to grade the outputs of your faster, cheaper models. But here’s the catch—you have to evaluate the evaluator. Hamel shows you exactly how to ensure your “Judge” isn’t hallucinating either.

3. The Taxonomy of Failure

You’ll learn to categorize errors. Is it a retrieval failure (RAG issue)? Is it a reasoning failure? Is it a tone issue? Once you classify the error, you know exactly which knob to turn.

Price Comparison: Why Pay More?

Let’s talk numbers. The live cohort on Maven is fantastic if you have a corporate budget burning a hole in your pocket. But if you’re bootstrapping or paying out of pocket, the price is steep.

Who Needs This?

• AI Engineers: You’re tired of regressions. You want to deploy with confidence.
• Product Managers: You need to define “quality” quantitatively, not just qualitatively.
• Founders: You need to stop burning cash on tokens for features that don’t work.

Conclusion

You can keep guessing, or you can start engineering. The choice is yours.

The industry is moving fast. The engineers who know how to run Evals are the ones getting the high-paying roles and shipping the best products. Don’t get left behind relying on “vibes.”

Grab the course at coursestodownload.com and start building reliable AI today.

Frequently Asked Questions

Do I need to be a Python expert for this course?

Not an expert, but you should be comfortable reading and writing basic Python. The course focuses on concepts and architecture, but the implementation examples are code-heavy.

Is this course specific to OpenAI models?

No. The principles apply whether you are using GPT-4, Claude, Llama 3, or any other LLM. The framework is model-agnostic.

What tools does the course use?

The course is tool-agnostic. While they may demonstrate concepts using tools like LangSmith or Weights & Biases, the core teachings are about the methodology, which you can implement with open-source tools or even simple CSV files.

How is this different from generic “Prompt Engineering” courses?

Prompt engineering is about getting an answer. AI Evals is about measuring if that answer is actually good, consistently, at scale. It is the next level up in seniority.

Sale Page : https://maven.com/parlance-labs/evals