What is Prompt Engineering

“Garbage in, garbage out.” In other words, the better the prompt, the better the output.

From foundational concepts to advanced strategies, here’s your guide to acing prompt engineering:

Prompt Engineering Fundamentals

There are countless ways to communicate with a generative AI tool, and just as many ways it could respond. Because of this, the framing of a prompt, the specific words you choose, and the examples you provide directly impact the quality of the output you’ll receive.

The clearer and more well-structured your prompts are, the better the AI’s response will be.

How Prompt Engineering Works

That’s the what, now the how:

The Science Behind Effective Prompts

While interacting with a generative AI might sometimes feel like having a conversation with an articulate human, it’s important to remember what’s really happening under the hood. These models aren’t thinking or understanding in the same way we do. Instead, they operate based on mathematical patterns and probabilities learned from the massive datasets they’ve been trained on.

Think of it like this: when you issue a prompt, the AI analyzes it, breaks it down, and then predicts the most likely next sequence of “tokens,” the fundamental building blocks of language it understands (more on this in just a moment). It’s essentially a highly sophisticated form of pattern recognition and prediction.

So, how do you guide this process effectively? By providing the right context and constraints within your prompt, you’re steering the AI’s “thought process,” influencing which patterns and probabilities it prioritizes.

It’s similar to how you’d guide a search engine. Instead of just typing a broad keyword, you use specific keywords, operators, and filters to narrow down the results and get closer to the information you need. Effective prompts act like those precise search queries for AI.

Understanding Context and Instructions

Crafting effective prompts hinges on two key elements: context and instructions. Imagine trying to give someone a task without telling them why they’re doing it or what the final outcome should look like. The results would likely be less than ideal. The same applies to AI.

Context provides the AI with the necessary background information. What is the goal of the output you’re seeking? Who is your target audience? What is the desired format (e.g., blog post, email, social media caption)? Providing this context helps the AI understand the overall purpose and tweak its response accordingly.

Instructions are your specific commands. Don’t assume the AI inherently knows what you want. Be explicit. Tell it what to do, what to include, and even what to avoid. The more clearly you articulate your needs, the better the chances of getting a relevant and useful result.

The power of prompt engineering truly shines when you combine clear context with explicit instructions. Consider these examples:

• Prompt lacking context and clear instructions: “Content about SEO.” (The AI has no idea what kind of content, for whom, or for what purpose.)
• Prompt with clear context and instructions: “Write a concise blog post (around 500 words) for SEO beginners explaining the importance of keyword research. Use a friendly and encouraging tone and include three actionable steps they can take to find relevant keywords for their website.” (Here, the AI has a clear goal, target audience, format, tone, and specific elements to include.)

Specifying elements like the desired role for the AI (“Act as a marketing expert”), the format of the output (“Generate a bulleted list”), and any constraints (“Keep it under 100 words”) can further refine the AI’s focus and improve the quality of its generation.

Tokens and Their Impact on Results:

Let’s touch back on the concept of tokens. Tokens are the fundamental building blocks that LLMs use to understand and construct words, sentences, and entire pieces of text. These tokens aren’t always whole words; they can be individual characters, parts of words, or even entire pieces of text.

For example, the two sentences you just read contain 294 characters and consist of 58 tokens. A general rule of thumb is that one token is typically the equivalent of 4-5 words, 294 characters divided by 58 is within that ballpark: 5.

Both your prompts and the AI’s outputs are measured in tokens. This is called tokenization, and it allows the AI to dissect human language into a format it can understand, analyze, and work with.

Most AI platforms have limits on the number of tokens you can use in a single interaction, which influences the cost of using these tools and the potential length of the content they generate.

Being mindful of token limits helps write concise and effective prompts. Sometimes, using fewer, more precise words can achieve a better result than a lengthy, rambling instruction.

8 Essential Prompt Engineering Techniques

Let’s now look at eight prompt engineering techniques to help you get the most out of your generative AI tool of choice:

Zero-Shot Prompting

Zero-shot prompting involves asking an AI tool to perform a task without providing any prior examples of what the desired output should look like. You’re relying solely on the AI’s pre-existing knowledge and understanding of a topic, which, depending on the task, isn’t always the best approach.

Why? Its training data has a shelf life. For example, OpenAI’s ChatGPT 4o was trained on data from July 2023, meaning it has no context of events after this data, leaving it without direct context for more recent events.

Plus, training datasets, while often massive, don’t contain every single piece of information. This means that for tasks requiring the very latest updates or highly specific, less common knowledge, zero-shot can fall short.

Zero-shot prompting isn’t without its strengths, though.

It can be surprisingly effective for simple tasks and accessing common knowledge that the AI model has likely encountered during its training. If the task is straightforward and the expected output is fairly standard, zero-shot prompting can be a quick way to get a result.

Few-Shot Prompting

Few-shot prompting involves providing the AI with a small number of examples (typically 2-5) that demonstrate the desired output format, style, and even the kind of information you’re looking for.

So, when a task is more complex or requires more nuance, providing a few examples helps the LLM understand your expectations more clearly. It learns from the patterns in your “shots” and is better equipped to generate a response that aligns with a user’s needs.

Chain-of-Thought Reasoning

Chain-of-thought (CoT) prompting guides the AI to break down a complex problem into a series of intermediate, logical steps before arriving at the final answer. Basically, you’re encouraging the LLM to “think out loud.”

Understanding CoT is valuable for grasping the reasoning of advanced AI, particularly in tasks requiring logical deduction or multi-step problem-solving. Its strength also lies in troubleshooting: you can review the AI’s step-by-step reasoning and correct any errors along the way.

Self-Consistency Techniques

Self-consistency involves generating multiple responses to the same prompt and then selecting the answer that appears most consistently across those responses or is deemed to be of the highest quality.

Think of self-consistency as your AI’s built-in second opinion. When you’re tackling a task with potentially many valid answers, this advanced technique lets you generate several responses and then identify the one that the AI “agrees” with most often. The result? For some use cases, it can return a better output.

System Prompts vs. User Prompts

Many AI platforms allow for two types of prompts: system prompts and user prompts. System prompts are instructions set by the developer or user that define the overall behavior, personality, and context of an AI tool. User prompts are the specific instructions given for a particular task.

Think of the system prompt as setting the stage, and the user prompt as the specific action within that stage.

Role-Based Prompting

Think of role-based prompting as giving your AI a specific hat to wear. Telling it to “Act as a seasoned marketing strategist” or “Explain this like a friendly tutor” allows you to influence its tone, the level of detail it provides, and even the kind of insights it offers, making the output far more applicable to your specific needs and audience.

Output Formatting Control

Output formatting control involves explicitly instructing the AI on how you want the generated content to be structured. For instance, you could specify the use of bullet points, numbered lists, tables, specific heading structures, and more.

Clearly defining the desired format improves the usability and readability of the AI-generated content, making it easier for both you and, if the output is for other people’s eyes, your audience to digest the information.

Temperature and Sampling Adjustments

Most AI platforms offer settings like “temperature” and “sampling” that influence the randomness and creativity of the AI’s output. Lower temperatures tend to produce more predictable and focused responses, while higher temperatures can lead to more creative and sometimes unexpected results.

Understanding these settings allows you to fine-tune the AI’s generation based on the task at hand. For factual content where accuracy is paramount, a lower temperature is usually preferred. For brainstorming or creative writing, a higher temperature might spark more innovative ideas.

Conclusion and Next Steps

The future of getting what you need from AI? It’s all about the prompts.

Use prompt engineering to shape prompts that get the best out of AI tools, and don’t be afraid to experiment.

The more you learn to speak AI’s language, the the responses will become.