MMatt Goren
← AI hub
Topic hub · 13 pieces

Evals & Quality

Knowing — not guessing — that your AI works.

FAQ6 min

AI for Operators: Frequently Asked Questions

Straight answers to the questions operators actually ask about AI: cost, headcount, where to start, quality, data safety, and ROI.

AI for Operators
Guide9 min

Automating Real Work With AI (Without the Slop)

A practical guide to automating real work with AI: pick the right tasks, keep a human in the loop, build the automation step by step, and gate the quality.

AI for Operators
Comparison8 min

Big Model vs Small Model: When Cheap and Fast Wins

Frontier model or small fast one? Quality, cost, latency, and reliability head to head, plus the fan-out-cheap, escalate-to-frontier pattern.

Models & Capabilities
FAQ6 min

Building With AI: Frequently Asked Questions

Practical answers for builders: model choice, RAG vs fine-tuning, agents, hallucinations, evals, cost, latency, and getting started with an LLM.

Building with LLMs
Pillar13 min

Building With LLMs: An Operator's Field Guide

How I actually build with large language models: model tiers, prompting as spec, structured output, evals, guardrails, and what breaks in production.

Building with LLMs
Guide9 min

Building an AI Content Engine From Scratch

The operator's blueprint for a real AI content engine: research substrate, draft, judge loop, AEO structure, schema, citation measurement, and feedback.

AI for Operators
Guide8 min

Evals: How to Actually Know Your AI Works

Vibes-testing lies to you. Here's how I build eval sets, grade outputs, and run regression tests so I know a model change didn't quietly break things.

Building with LLMs
Guide8 min

Guardrails: Shipping AI That Won't Embarrass You

Input and output validation, moderation, prompt-injection defense, grounding, human-in-the-loop, and logging — the layers that keep AI from going sideways in front of users.

Building with LLMs
Guide8 min

How to Cut Your LLM Costs (Without Cutting Quality)

Prompt caching, batching, model routing, leaner context, output caps — the levers that drop your AI bill without touching output quality.

Building with LLMs
Comparison7 min

In-House AI Content vs Hiring It Out

Build the AI content engine yourself or hire an agency? A clear breakdown of cost, control, quality, and what to never outsource.

AI for Operators
FAQ7 min

Models & Capabilities: Frequently Asked Questions

Straight answers to the questions builders actually ask about LLMs: tokens, context windows, cost, hallucination, multimodality, and more.

Models & Capabilities
Guide10 min

Programmatic AEO at Scale (Without Becoming Slop)

How to build hundreds of templated pages that stay genuinely useful and citable — the quality gates that separate leverage from spam.

AI Search & AEO
Guide9 min

Prompt Engineering for Production (Not Party Tricks)

Treat prompts as specifications, not magic words. Structure, structured output, evals, versioning, and the system prompts that run 10,000 times a day.

Building with LLMs