Artificial Intelligence
5 ARTICLES IN THIS CATEGORY
Practical AI engineering — prompt optimization, agent workflows, and integrating LLMs into production applications.
AI Agents Need Artifacts, Not Activity
I got tired of AI agents sounding busy and leaving nothing behind. Here's the boring rule I now use before calling agent work done.
Stop Making AI Agents Hand You Markdown Walls
I got tired of agents handing me giant Markdown blobs I would never read properly. So I built MaraDocs to turn agent reports into durable HTML artifacts.
How AI Agents Actually Work
AI agents aren't magic. They're a loop. Here's the full picture — with a live dashboard you control.
How to Train Your AI
Spoiler: you probably don't need to. Here's what actually happens after you integrate an LLM API, from whispering to a dragon to raising one from an egg.
You no longer need prompt engineering course
I found a better way to make prompts better
About this category
Practical AI engineering — not hype. These articles cover what it actually takes to build with large language models in production: designing agent workflows that plan and execute real work, structuring prompts that stay reliable as requirements change, and fine-tuning models when off-the-shelf behavior isn't enough.
What you'll find here
- ›AI agents — how autonomous agents plan, call tools, and loop toward a goal, and where they break down in the real world.
- ›LLM integration — patterns for wiring models into applications: retrieval, structured output, and keeping latency and cost under control.
- ›Prompt & context engineering — making model behavior predictable without brittle prompt-hacking.
- ›Fine-tuning & training — when to reach for a custom model and how to get there without a research team.
Everything here is written from hands-on experience shipping AI features, with runnable examples and the trade-offs spelled out.