AI Guides & Tutorials
When a model is not doing what you want, there are three levers people pull: better prompting, retrieval, or fine-tuning. They are wildly different in cost and effort, and teams reach for the expensive one far too early. Here is how to pick the right lever without burning your budget on the wrong one.
Read more: Fine-tuning, prompting, or RAG: pick the right tool and save the money
Most AI bills are not big because the work is hard. They are big because of lazy defaults: the most expensive model on every task, the biggest context every time, and no thought about which jobs actually need the premium option. Here is how to spend far less without your results getting worse.
Read more: How to cut your AI bill in half without downgrading your results
Every model has a default voice, and it is the same voice: smooth, agreeable, faintly corporate, allergic to a strong opinion. It is the tone of a brand apologizing. If you want AI to help you write without sounding like everyone else who uses AI, you have to actively drag it away from that default. Here is how.
Read more: Getting AI to write in your voice instead of its own
AI coding tools are genuinely great and genuinely dangerous, often in the same suggestion. They will write in thirty seconds something that would have taken you twenty minutes, and it will contain a subtle bug you would never have written yourself. Here is the workflow I actually use to get the speed without shipping the mistakes.
Read more: A practical workflow for coding with AI without shipping its mistakes
Every model launch comes with a chart where the new model is tallest. The charts are technically true and practically useless, because they are marketing wearing a lab coat. Here is how to read a benchmark like a skeptic, so a leaderboard never again talks you into the wrong model for your actual work.