Prompting
Specifying tasks so the output you want is the obvious continuation.
6 Prompting
- What Is a Context Window? One token budget covering your prompt, the conversation history, retrieved documents, and the answer being generated. Everything competes for the same space.
- Why Do AI Models Hallucinate? Fluent, confident, wrong. Hallucination is not a bug in the system — it follows directly from what the system was built to do.
- Prompt Basics Not magic words. Prompting is task specification, and four reusable structures cover most of what works.
- Making a Model Think Step by Step Written-out reasoning is not a psychological trick. Each token gets one forward pass, so intermediate steps are genuine additional computation.
- Show, Don't Tell: Few-Shot Prompting Two examples communicate more than three paragraphs of instruction. Why in-context learning works and how to build examples that carry their weight.
- In-Context Learning: Why Few-Shot Works Show a model a few examples in the prompt and it improves on the spot, with no weight change. Here is the theory of why that happens and where it breaks.
- Your Prompt Isn't Working. Now What? Four distinct failure types wear the same costume. Diagnosing which one you have saves the hours usually spent rewording a prompt that was never the problem.
- Prompt Patterns and Anti-Patterns A small set of prompt structures solves most tasks, and a smaller set of mistakes causes most failures. The reusable shapes, and the ones to avoid.
- Getting JSON Out of an LLM Asking politely for JSON gets you JSON most of the time. Most of the time is not good enough for code, and there are ways to make it guaranteed.
- What Is a System Prompt? Instructions set once at the top of a conversation, in a structurally distinct slot the model was trained to weight differently.