Should You Give AI Examples or Instructions? How To Get Better Results From Your Tools

Several strategies help marketers get better results from AI, and we’re far enough along with the technology that I’d imagine most people have their preferred ways of working with it to get results they’re content with.

Even still, I’ve been wondering: Is it better to tell AI what you want, or show it? Or both?

Each approach can work, but they communicate expectations differently: explicit instructions vs. patterns to follow.

These techniques are commonly called instruction-based prompting and few-shot prompting. And understanding when to use each (or both together) can indeed make a difference in the quality of the output.

Here’s how they work, where each is most useful and how to combine them for better results.

Instruction-Based vs. Few-Shot Prompting

The main difference between instruction-based and few-shot prompting comes down to how you communicate what you want AI to produce.

With instructions, you describe the task and its requirements. With few-shot prompting, you provide examples that demonstrate the type of output you’re after.

Neither is necessarily better than the other — they just give AI different kinds of information to work with.

What Is Instruction-Based Prompting?

Instruction-based prompting tells an AI model what to do using explicit directions. You might specify the task, intended audience, desired tone, formatting requirements or other constraints.

For example:

“Write a meta description for a blog about email marketing automation. Keep it under 155 characters, include the phrase „email marketing automation“ and use a conversational, informative tone.”

That’s fairly straightforward: there’s a defined task, a few rules to follow and enough information to produce a reasonable result.

You can make instructions as simple or detailed as you’d like. But generally, the more specific your requirements, the more useful it is to spell them out clearly.

What Is Few-Shot Prompting?

Few-shot prompting takes a different approach. Instead of relying entirely on written instructions, you give AI a few examples that demonstrate the desired output. The model then uses those examples as context to identify patterns and produce something similar.

For instance, you could provide three previously approved email subject lines and ask AI to generate five more in the same style.

There are a couple of other versions of “_-shot” prompting, too:

• Zero-shot prompting: Asking AI to complete a task without providing examples.
• One-shot prompting: Providing one example.
• Few-shot prompting: Providing a handful of examples.

An important note: These terms describe the number of examples included, not necessarily whether the prompt also contains instructions. You can absolutely provide detailed instructions alongside a few examples (and that’s often — but not always — the best play).

When Should You Give AI Instructions?

Instructions tend to work well when you 1) know exactly what you need, and 2) can describe it clearly.

Think about the more structured tasks in a typical marketing workflow: writing metadata, summarizing research, extracting information from documents or turning campaign notes into an organized outline. These tasks usually have fairly straightforward requirements, i.e., you know what the finished product should contain, how it should be formatted and what constraints it needs to meet.

That makes instructions especially useful for tasks with:

• Defined formats: Tables, lists, summaries, outlines and structured data.
• Specific constraints: Word counts, character limits, required keywords or prohibited language.
• Repeatable tasks: Workflows that need to follow the same rules consistently.
• Clear objectives: Tasks where success is relatively easy to describe and evaluate.

For example, if you’re asking AI to summarize an interview, you might instruct it to identify the three most common challenges, include supporting quotes and avoid making assumptions beyond the transcript.

There’s probably no need to provide several examples of previous interview summaries unless you have a particular reporting style or format to replicate.

The advantage of instructions is that they make your expectations explicit. You’re not asking the model to guess anything. Even still, instructions have limitations. Some preferences are difficult to put into words, particularly when you’re dealing with tone, creativity or brand voice. More on that below.

When Should You Give AI Examples?

Have you ever tried explaining your brand’s writing style to an AI tool? It doesn’t always get it right.

Instructions like “sound professional but conversational,” “confident but not overly promotional,” or “friendly without being too casual” are perfectly reasonable, but they’re also a bit subjective.

What sounds conversational to one person might sound overly familiar to another. And an AI model’s interpretation of „professional“ might differ between models or involve considerably more corporate jargon than you’d like.

Examples can help communicate subtleties like these. Instead of spending several paragraphs describing a preferred writing style (only for it to likely still miss the mark by some measure), just provide a few examples of pitch-perfect published content that already gets it right.

Few-shot prompting can be particularly helpful for:

• Brand voice: Demonstrating tone, vocabulary, sentence structure and stylistic preferences.
• Creative content: Showing the type of humor, storytelling or messaging you want.
• Recurring content formats: Replicating established patterns across newsletters, social posts or campaign assets.
• Complex classifications: Demonstrating how to categorize or label information when the distinctions aren’t obvious.

If you’re drafting LinkedIn posts for a company executive, you could explain that the posts should sound knowledgeable, personable and direct. But showing AI three posts that that executive actually wrote gives it something more concrete to work with.

It can identify difficult-to-describe characteristics, like how the person tends to open a post, transition between ideas or express opinions.

Why Combining Instructions and Examples Often Works Best

Most AI-adept markers likely already know that instructions and examples complement each other, because they address different parts of a task. Instructions establish requirements and boundaries; examples demonstrate how those expectations should translate into an actual output.

This is particularly useful for marketing tasks that involve both objective requirements and more subjective preferences. You might know exactly what information a piece of content needs to include, how long it should be and who it’s intended for. But describing the precise tone, pacing or writing style you’re after can be considerably more difficult. Providing examples alongside your instructions helps fill in those gaps.

Combining both approaches also gives you more control over how AI interprets your references. Rather than leaving the model to decide which patterns to follow, you can explicitly identify the characteristics you want it to replicate, and those it should ignore.

In general, when it comes to AI, the best results often come from reducing ambiguity wherever possible.

How To Combine Both for Better Prompts

Combining instructions and examples doesn’t require a complicated framework, especially since those who use AI are very used to prompting nowadays anyway. But here are some straightforward tips to try:

1. Define the Task

Explain what you want AI to accomplish, and be specific about the deliverable, including details like its purpose and the intended audience.

2. Establish Your Requirements

Specify any rules the output needs to follow, like length, format, tone, required information or language to avoid. Prioritize genuinely important requirements rather than creating an unnecessarily long list of preferences. You can always ensure and edit for the others later during your editing pass.

3. Provide Relevant Examples

If the task would benefit from a stylistic or structural reference, include one to three examples, choosing ones that most closely resemble the type of output needed.

4. Explain What the Examples Demonstrate

This step is easy to overlook, but can make a big difference: Tell the AI tool exactly what you want it to learn from the provided references.

Should it follow the tone? Formatting? Detail? Information hierarchy?

In addition to telling it what it should learn from those examples, identify anything it shouldn’t imitate.

5. Review and Refine

Evaluate the output against your original requirements. AI is far from perfect, and it could miss something.

If it does get something wrong, consider revising a requirement, replacing an example or clarifying which characteristics matter most.

And remember that even a well-constructed prompt doesn’t guarantee an accurate or publication-ready result. Always fact-check and have a human review.

Better Prompts Start With Clear Expectations

Instruction-based and few-shot prompting both have their ideal use cases. And while there’s no objective winner, outputs are often better when you put them together!

If you’re working on a straightforward task with well-defined requirements, instructions might be all you need. If you’re trying to replicate a particular style or demonstrate a nuanced preference, examples can provide useful context. Most tasks can benefit from both.

The real winner here is clarity. For your most important tasks, tell AI what needs to happen, show it what a good result looks like, and make clear which parts of the examples it should heed.

Note: This article was originally published on contentmarketing.ai.