Technical articles, tutorials, and thoughts on software engineering
Full-Stack Development
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.
AI Automation
Hallucination can't be fully eliminated, but specific prompt and system design choices reduce it substantially.
RAG lets an LLM answer questions using your specific documents and data, instead of relying only on what it learned during training.
Traditional automation follows rigid rules. AI automation adds language understanding, so it can handle the unstructured inputs that broke your old RPA setup.
Prompt Engineering
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.
AI Agents
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.
