15 ChatGPT Hacks Every Beginner Should Know
15 simple ChatGPT tricks for total beginners, each with a copy-paste prompt you can use right now. No tech background needed.
AI Prompts That Feel Like Cheating
10 copy-paste AI prompts for everyday wins: the awkward email, dinner from your fridge, a confusing contract, a hard conversation, and more.
Building With Claude: Strengths, Quirks, and How to Get the Most Out of It
How I build with Claude in production: where it shines, which tier to use, prompt caching, structured output, extended thinking, and the honest limits.
Prompting Claude vs GPT: What Actually Differs
The prompting habits that carry between Claude and GPT, the ones that don't, and how each family wants to be steered in production.
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.
Context Engineering: The Skill That Replaced Prompt Hacking
Managing the context window is the real craft now. What to put in, retrieval vs stuffing, ordering, caching, compaction, token budgets, and multi-turn memory.
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.
How to Track Whether AI Is Citing You
A repeatable method to measure AI-search visibility: build a prompt set, query the engines, log citations, score share-of-voice, and turn the gaps into content.
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.
Single Prompt vs Agent vs Workflow: Choosing the Right Shape
The three shapes of LLM apps — one call, an agent loop, a deterministic workflow. How they compare and how to pick the simplest one that works.
When Fine-Tuning Is Actually Worth It
The honest cases for fine-tuning versus prompting, RAG, and long context — plus the maintenance cost that's why most teams shouldn't start here.