The quality of an LLM’s answer depends heavily on the quality of the instruction. Prompt engineering simply means writing instructions that produce reliable, useful results.
A simple structure that works
- Role: tell the model who it is acting as, for example a support agent for a software company
- Task: state exactly what you want done
- Context: supply the facts it needs, such as policies or product details
- Format: specify the output, such as a three-bullet summary or a table
- Constraints: say what to avoid, such as guessing or inventing details
Show examples
One or two examples of a good answer often improve results more than a long description. Examples show tone, length and structure.
Tell it what to do when unsure
Instruct the model to say when it does not know or when information is missing, rather than filling gaps. This reduces made-up answers.
Test and refine
Try your prompt on many realistic inputs, not just one. Keep a record of versions and results so improvements are measurable.
Prompts are only part of the system
For business use, combine prompts with your own data, validation checks and human review for important outputs.
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