What Is Prompt Engineering? A Practical Guide With Examples
Prompt engineering is the skill of writing inputs that get reliable results from AI models. Learn the parts of a good prompt, with before-and-after examples.
Fundamentals
Prompt engineering fundamentals are the basics that decide most results: say exactly what you want, give the model the context it cannot guess, set constraints such as length, audience and format, and describe what a good answer looks like. Most weak outputs come from a vague task, not from a weak model.
Your app parses the model’s JSON, but sometimes the reply starts with “Sure! Here is the JSON:”. What is the most robust fix?
A prompt says “Summarize this report.” The summaries keep coming back too long and too general. What is the best first fix?
B. State the reader, the purpose and a length, e.g. “5 bullets for a CFO deciding on budget” “Be concise” is still vague. Naming who reads the summary, what they need it for and how long it should be gives the model something concrete to aim at.
A clear task, the context the model needs, constraints (length, audience, tone, format) and, when it helps, an example of the output you want. Put the most important instruction where it cannot be missed.
No. The fundamentals are about writing clear instructions. TokIQ’s Fundamentals questions use everyday tasks like summaries, emails and analysis.
Prompt engineering is the skill of writing inputs that get reliable results from AI models. Learn the parts of a good prompt, with before-and-after examples.
A practical checklist for writing better ChatGPT prompts, with before-and-after examples. The same habits work for Claude, Gemini and other AI assistants.
The prompt engineering mistakes that cause most bad AI output, from missing context to conflicting rules and untested changes, each with a concrete fix.
Short quizzes on real prompting decisions, with an explanation for every answer. Free to start on iPhone and Android.