Most people’s first attempt at prompting an AI model looks like a quick question typed the way you’d ask a colleague. That works for casual use, but it’s not reliable enough for anything you depend on. Here are five techniques that consistently improve output quality.
1. Be Specific About the Output Format
“Summarize this” gives you an unpredictable summary. “Summarize this in three bullet points, each under 20 words, focused on action items” gives you something you can actually use without editing. Vague instructions produce vague, inconsistent output — the model fills in the gaps however it sees fit.
2. Give It Examples (Few-Shot Prompting)
Showing the model one or two examples of exactly the input/output pattern you want is often more effective than describing the pattern in words. If you want a consistent tone or structure across many outputs, a good example does more work than a paragraph of instructions.
3. Separate Instructions From Data Clearly
When you’re asking a model to process a chunk of text — a document, a transcript, an email — clearly mark where the data starts and ends, usually with delimiters or headers. This reduces the chance the model confuses your instructions with the content it’s supposed to be processing.
4. Ask It to Reason Before Answering
For anything involving multiple steps or judgment calls, prompting the model to work through its reasoning before giving a final answer tends to produce more accurate results than asking for the answer directly. You can also strip the reasoning out of what you show the end user afterward.
5. Iterate Instead of Expecting the First Try to Be Perfect
Treat your first prompt as a draft. If the output is close but not right, tell the model specifically what to change rather than starting over — “make it shorter” or “remove the second paragraph” gets you there faster than rewriting the whole prompt from scratch.
These techniques are simple individually, but combining them reliably — and knowing which one to reach for in a given situation — is a skill that takes deliberate practice. That structured practice, with feedback, is what our Prompt Engineering course is built around.