How to Design Evaluators That Catch What Actually Breaks

The practical guide to code-based checks, LLM judges, and rubrics for real-world AI apps

Mar 3 • Paolo Perrone

14

2

2

February 2026

Start Here: Your Map to Decoding AI

The AI Engineering Command Center

Feb 28 • Paul Iusztin

23

8

3

I Spent 9 Months Building an Agentic AI Engineering Course

Google is already recommending it alongside Coursera, DeepLearning.AI and Oxford.

Feb 26 • Paul Iusztin

19

2

2

Generate Synthetic Datasets for AI Evals

5 strategies from cold start to 450 diverse inputs in minutes

Feb 24 • Paul Iusztin

24

7

4

Thank You for Supporting Decoding AI

Roadmap updates and the new DAI chat.

Feb 19 • Paul Iusztin

1

No Evals Dataset? Here's How to Build One from Scratch

Build evaluators to signal problems that users actually care about. Step-by-step guide.

Feb 17 • Paul Iusztin

23

3

Integrating AI Evals Into Your AI App

The holistic guide: From optimization to production monitoring

Feb 10 • Paul Iusztin

29

5

4

Behind the Scenes of AI Observability in Production

What actually works after 6 months of trial and error

Feb 3 • Alejandro Aboy

27

5

6

January 2026

Your Agent's Reasoning Is Fine - Its Memory Isn't

Using GraphRAG to build a Production Engineer agent that knows dependencies, incidents, and ownership.

Jan 20 • Anca Ioana Muscalagiu

63

5

15

How to Design Python AI Projects That Don't Fall Apart

A framework-agnostic approach to modular agents, workflows, and LLM apps using the pragmatic clean architecture design.

Jan 13 • Paul Iusztin

59

4

12

The Realistic Guide to Mastering AI Agents in 2026

From zero to production in 6–9 months. What to learn, what to skip, and why most tutorials fail.

Jan 6 • Paolo Perrone

89

5

12

December 2025

From 100+ AI Tools to 4: My Prod Stack

How simplicity beats complexity in real AI systems

Dec 30, 2025 • Paul Iusztin

58

12

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