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AI Evals & Observability

The AI Evals Roadmap I Wish I Had
From vibe checking to trusted agents in production
 • Paul Iusztin
Why RAG Has Exactly 6 Failure Modes. No More, No Less.
A complete guide for evaluating your retrieval-augmented generation systems.
 • Paul Iusztin
Our LLM Judge Passed Everything. It Was Wrong.
Align your evaluator with human judgment, or don't trust it at all.
 • Paul Iusztin
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
 • Paolo Perrone
Generate Synthetic Datasets for AI Evals
5 strategies from cold start to 450 diverse inputs in minutes
 • Paul Iusztin
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.
 • Paul Iusztin
Integrating AI Evals Into Your AI App
The holistic guide: From optimization to production monitoring
 • Paul Iusztin
Behind the Scenes of AI Observability in Production
What actually works after 6 months of trial and error
 • Alejandro Aboy
Stop Launching AI Apps Without This Framework
A practical guide to building an eval-driven loop for your LLM app using synthetic data, before you have users.
 • Hugo Bowne-Anderson
Escaping POC Purgatory: Evaluation-Driven Development for AI Systems
A new software development life cycle for LLMs
 • Hugo Bowne-Anderson and Stefan Krawczyk
The 5-Star Lie: You Are Doing AI Evals Wrong
Why binary evals are better than likert scales
 • Hamel Husain
The Mirage of Generic AI Metrics
Why off-the-shelf evals sabotage your AI product
 • Hamel Husain
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