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Skylar Payne

Technical library

Build AI systems that actually improve.

This is the front door for the deeper technical work: evals, reliability, agents, RAG, observability, and the operating loops that keep AI products from becoming a pile of clever demos.

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The shortest useful path

Open the evals hub →

1. Understand the loop

Good evals are not a score. They are the feedback loop for making product behavior better.

2. Find the failure modes

Trace review, taxonomies, and regression sets are how vague “quality” becomes fixable work.

3. Ship the operating system

The durable win is a repeatable review → change → measure loop, not one heroic prompt rewrite.

Core topics

AI Evals

Evaluation loops, failure taxonomies, AI judges, and review systems that teams actually use.

AI Reliability

Patterns for turning prototypes into systems that stay observable, debuggable, and safe under real use.

Agents

Multi-step workflows, tool use, approval gates, memory, state, and traces.

RAG

Retrieval quality, grounding, latency, evaluation, and the places RAG still breaks.

TIL / short notes

Small durable lessons from real work: weird eval failures, implementation tricks, debugging patterns, and corrections to naive mental models.

View all short notes →

No TILs are published yet. The site now has a first-class lane for them when the first one is ready.

Series

Practical AI Evals

A practical reading path for building evals that improve product behavior instead of becoming dashboard theater.

Effective AI Engineering

A curated path through reliability, observability, RAG, agents, guardrails, and production feedback loops.

Latest writing

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