What Is a Large Language Model? A Large Language Model (LLM) is a type of AI trained on massive amounts of text data to predict the next most likely token in a sequence. That simple mechanic — predict the next token — is the foundation of everything from ChatGPT to Claude to Llama. ...
The first two posts in this series covered the mechanics (tokens, temperature, the four pillars) and the cost angle (why a sloppy prompt is a budget line, not just an annoyance). This one is the field guide that was implied but not fully delivered: a working set of bad/good/best examples across the techniques that actually separate a prompt that gets lucky once from one that reliably works. ...
Most people talk to an LLM the way they’d talk to a search engine: type a few words, hope for the best, complain when the answer is generic. That’s not a model problem. It’s a prompting problem. ...
Every token an LLM API processes has a price tag, on the way in and on the way out. That fact turns “write better prompts” from a productivity tip into a line item. Most organizations treat AI spend as an unavoidable cost of doing business; in practice, a meaningful chunk of it is just badly engineered prompts paying for their own inefficiency. ...
Introduction LLM applications broke a lot of assumptions security teams had spent two decades building. Input validation, output encoding, least privilege — all still relevant, but none of it was designed for a system that treats instructions and data as the exact same thing. The OWASP Top 10 for LLM Applications exists to give that gap a shared vocabulary. ...
If you’ve followed the Self-Hosted AI Stack walkthrough, you’ve got Ollama running locally, OpenClaw as your agent UI, and the whole thing locked down behind Tailscale. That’s a solid foundation. But a chat interface, however useful, isn’t the ceiling of what this stack can do. ...
There’s a certain appeal to running your own language model. No API costs, no data leaving your network, no rate limits, no terms of service to worry about when you feed it sensitive context. For anyone who works in security — or just values privacy — the idea of keeping inference local is worth the setup cost. ...
Every major cloud provider has an AI product now. Most of them are genuinely useful. They’re also asking you to send your data — your queries, your context, your documents — to infrastructure you don’t control, operated by a company whose incentives around data retention may not align with yours. ...