AI Prompts for Lawyers: The Ones That Actually Save You Time
Copy-and-paste AI prompts for the real work of lawyering, including five most attorneys never think to try — steelman the other side, stress-test your argument against a skeptical judge, turn rambling call notes into a clean issue list.
Building eval datasets that catch regressions
Your eval set is only as good as the cases in it. Here's how I source real failures, write pass/fail criteria that hold, and keep the set honest as it grows.
LLM Observability: Tracing, Logging, and Evals in Production
Normal APM tells you the request succeeded. It can't tell you the answer was wrong. Here's how I trace, log, and evaluate LLM apps in production.
Training Your Team on AI: Driving Real Adoption, Not Just Licenses
Buying seats doesn't create usage. A practical playbook for driving real AI adoption on your team — jobs first, a shared prompt library, champions, guardrails, and measuring who actually uses it.
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.
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.
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.
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: 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 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.
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.
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.
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.
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.
Models & Capabilities: Frequently Asked Questions
Straight answers to the questions builders actually ask about LLMs: tokens, context windows, cost, hallucination, multimodality, and more.
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.
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.