Prompt Engineering Examples: What Great Prompt Engineering Looks Like
Great prompt engineering looks less like a clever one-liner and more like a well-specified brief — the kind you’d hand a sharp junior writer who’s never worked with your brand before. Below are illustrative examples of strong prompt patterns across common marketing tasks, along with what makes each one work.
- What Separates a Great Prompt From an Average One
- Example: A Research Synthesis Prompt That Actually Grounds Claims
- Example: A Brand Voice Prompt That Doesn't Sound Generic
- Example: A Structured FAQ-Generation Prompt for AI Search
- Example: A Comparison and Analysis Prompt for Strategic Content
- Example: A Constraint-Heavy Prompt for High-Volume Local Content
- Anti-Examples: Prompts That Look Fine but Fail in Practice
- Example: An Iteration Prompt for Fixing a Weak Draft
These are composite, illustrative examples built to demonstrate technique, not verbatim client prompts. The structure and reasoning transfer directly to real work.
What Separates a Great Prompt From an Average One
Average prompts describe a topic. Great prompts describe a topic, an audience, a purpose, a structure, a tone, and the specific facts or reference material the response should be grounded in. The difference isn’t length for its own sake — a great prompt can still be short — it’s completeness relative to what the task actually requires.
Across every example below, notice the same underlying pattern: real specificity replaces vague adjectives, structure is requested explicitly rather than hoped for, and the prompt names what to avoid as clearly as what to include.
It also helps to notice what great prompts don’t do. They rarely try to accomplish five things at once. A prompt asking for a fully researched, perfectly toned, structurally precise, SEO-optimized piece in a single pass is usually asking for more than any single instruction can reliably deliver. The strongest practitioners break big tasks into smaller, well-specified prompts rather than writing one enormous instruction and hoping the model juggles every requirement correctly.
Example: A Research Synthesis Prompt That Actually Grounds Claims
A weak version simply asks for “a summary of current SEO best practices.” A strong version instead provides three or four specific source excerpts directly in the prompt, then instructs the model to synthesize only from that provided material, flagging explicitly if a claim can’t be supported by the given sources.
This grounding technique is the single most effective defense against hallucination in research-heavy marketing content. The model isn’t being asked to recall facts from memory — it’s being asked to synthesize material it’s actually been handed, which is a fundamentally more reliable task.
Example: A Brand Voice Prompt That Doesn't Sound Generic
A weak brand voice prompt says “write in a friendly, professional tone.” A strong one includes two or three short real examples of the brand’s past writing directly in the prompt as style reference, plus a specific negative instruction such as “avoid exclamation points and avoid opening with a rhetorical question.”
The inclusion of real reference examples does more work than any adjective ever could — models imitate concrete patterns far more reliably than they interpret abstract descriptors, which is why “friendly and professional” produces wildly different results depending on the model and the day.
Example: A Structured FAQ-Generation Prompt for AI Search
A weak prompt asks for “an FAQ section about our services.” A strong prompt specifies that each question should reflect a genuine query a real customer would type or ask a search engine, that each answer should lead with the direct answer in the first sentence before any elaboration, and that no two questions should overlap in what they’re actually asking.
This structure matters more now than it did a few years ago, because AI-generated search summaries tend to extract and cite content that answers a question cleanly and immediately, rather than content that builds up to an answer gradually.
Example: A Comparison and Analysis Prompt for Strategic Content
A weak prompt asks the model to “compare these two approaches.” A strong prompt specifies the exact criteria the comparison should weigh — cost, implementation time, and long-term maintenance burden, for example — and explicitly instructs the model to state a clear recommendation rather than a noncommittal “it depends” that leaves the reader no better off.
Naming the comparison criteria upfront prevents the common failure where a model produces a comparison that reads as balanced but is actually shallow, touching on surface differences without engaging the dimensions that actually matter to the decision.
Example: A Constraint-Heavy Prompt for High-Volume Local Content
A weak prompt for local business content invites exactly the thin, templated output that hurts rankings: “write a paragraph about our plumbing services in each of these five cities.” A strong prompt instead requires a specific local detail be included for each city — drawn from a provided brief, never invented — and caps the number of generic service-benefit sentences allowed per piece, forcing the local specificity to carry more of the content’s weight.
- Required input: a real, brief-sourced local detail per location, not model-invented color.
- Hard constraint: a limit on generic, city-agnostic sentences to prevent templated filler.
- Explicit ban: no reuse of the same opening sentence structure across locations.
Anti-Examples: Prompts That Look Fine but Fail in Practice
Some of the most instructive examples are the ones that seem reasonable on first read but reliably underperform. “Make this more engaging” is a common one — it gives the model no concrete direction, so it tends to add filler enthusiasm rather than genuine improvement. “Write like a human” is another — every model already tries to do this by default, so the instruction adds nothing actionable.
“Summarize everything you know about X” is a quiet hallucination risk, since it invites the model to draw on unverified general knowledge rather than grounded, checkable material. The fix in every case is the same: replace the vague instruction with a specific, checkable one — name the exact change you want, or provide the exact material to draw from.
Another common anti-example: prompts that pile on contradictory instructions, like asking for content that’s simultaneously “concise” and “comprehensive” with no guidance on which should win when they conflict. The model resolves the contradiction on its own, usually inconsistently between attempts, which is exactly the kind of unpredictability a well-engineered prompt is supposed to eliminate. When two goals are genuinely in tension, name the priority explicitly rather than leaving it for the model to guess.
Example: An Iteration Prompt for Fixing a Weak Draft
A weak revision request says “make this better.” A strong one names the specific problem: “this draft buries the direct answer in the third paragraph — move the core answer to the first two sentences, then keep the supporting detail below in the same order it currently appears.” This kind of targeted revision prompt is often more valuable than the original drafting prompt, because it teaches the model, and the prompt engineer, exactly what “better” means in a specific, transferable way.
Saving these targeted revision prompts alongside the original drafting prompt in a shared library turns every edit into reusable knowledge, rather than a one-off fix that has to be rediscovered the next time a similar weakness shows up in a different piece.
Frequently Asked Questions
What's the fastest way to spot a weak prompt before using it?
Check whether it describes an audience, a purpose, and a structure, or just a topic. A prompt that only names a topic is almost always going to produce generic output regardless of the model used.
Why does including real examples work better than describing tone with adjectives?
Models imitate concrete patterns far more reliably than they interpret abstract descriptors like "friendly" or "professional," which can be read many different ways depending on context.
Is grounding a prompt in provided sources always necessary?
It's especially important for research-heavy or factual content where hallucination risk is high. For purely creative or stylistic tasks, grounding matters less than clear structural and tonal instructions.
Why do vague instructions like "make it more engaging" tend to fail?
They give the model no concrete, checkable direction, so it typically responds by adding generic enthusiasm or filler rather than a genuine, targeted improvement.
Can these example patterns be combined in a single prompt?
Yes — most strong real-world prompts combine several of these techniques at once, such as grounding plus structure plus explicit negative instructions, rather than relying on just one technique alone.
Terry has 30+ years in software and SEO. He’s the founder of Salterra Digital Services and SEO Spring Training, host of the Roundtable SEO Mastermind, and lead instructor at SEO University — teaching the exact tactics his team uses on client work.
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