Prompt Engineering

A good prompt isn't a random sentence — it has parts (instruction, context, examples, output) and their order matters.

The biggest cause of bad output is a vague prompt — clarity saves you many correction rounds.

Giving one or two examples steers output format faster than any long description.

The system message sets the model's persona and standing rules for the whole conversation — the strongest place to shape behaviour.

Asking the model to reason step by step before answering sharply improves accuracy on logic and math tasks.

If your app parses the model's reply in code, it must come out in a fixed, parseable shape — not free text.

Most tasks fall into recurring patterns — knowing them lets you solve new problems much faster.

Models sometimes invent facts confidently — prompting techniques cut this and make answers verifiable.

Your first prompt is rarely the best — real engineering is systematic experimentation and measurement, not guessing.

Complex tasks work best split into a sequence of prompts where each output feeds the next.

Combining reasoning with external tool calls lets the model search, compute, and act — not just write.

Any prompt open to users can be attacked via injection — you must defend your app from day one.