Technical articles, tutorials, and thoughts on software engineering
The most effective AI-powered products are rarely built by an AI specialist alone — they need the full-stack discipline to actually ship as real software.
Traditional automation follows rigid rules. AI automation adds language understanding, so it can handle the unstructured inputs that broke your old RPA setup.
If your LLM application processes any external or user-supplied content, prompt injection is a real security concern — not a theoretical one.
You can't reliably improve a prompt you haven't measured. Here's how to build a lightweight evaluation system from scratch.
Asking a model to reason step by step before answering measurably improves accuracy on tasks that require multi-step logic.
Showing an LLM a few examples of the pattern you want is often more effective than describing it. Here's how to do it well.
Less mystical, more methodical: a look at the actual day-to-day work behind making LLM features reliable.
Prompt engineering is the discipline of designing inputs to get reliable, accurate outputs from an LLM. It's less about clever wording than systematic testing.
Giving an AI agent the ability to act means designing exactly what it's allowed to do alone — and what it isn't.
The gap between an impressive agent demo and a production-ready system is wider than most teams expect. Here's what actually breaks.
Beyond the hype, here are ten concrete, currently-viable applications of AI agents across common business functions.
Under the hood, every AI agent is built from the same three pieces: a planning loop, a set of tools, and some form of memory.
Agentic AI is the umbrella term for systems where AI doesn't just generate content — it pursues goals. Here's the plain-English version.