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. ...
Why Prompt Engineering Matters The same model will give you dramatically different results depending on how you phrase your request. Prompt engineering is the practice of structuring your input to get consistently useful output. ...
Threat Hunting “You are a threat hunter. Given the following network logs, identify any patterns consistent with lateral movement, beaconing, or data exfiltration. Think step by step before giving your findings.” ...
Hallucination LLMs generate confident-sounding text that may be factually wrong. In security contexts this is dangerous. A model might: Fabricate a CVE number that doesn’t exist Invent tool flags or command syntax Cite statistics with no real source Describe a vulnerability incorrectly Mitigation: Always verify AI-generated technical claims against primary sources — NVD, vendor advisories, official documentation. ...
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. ...
Every AI model is, at its core, a direct product of its training data. Long before a system answers its first prompt or makes its first real-world prediction, decisions about data collection, sourcing, and processing have already permanently shaped its behavior. ...
In Part 1 we looked at how little organizations actually know about where their AI training data comes from. This time, we look at what happens once that unverified data gets baked directly into a model. ...
Parts 1 and 2 of this series covered the data going into a model. This time we look at the technical choices made while training and optimizing it — decisions that introduce their own, separate set of security trade-offs. ...