Understanding Prompt Engineering: How to get Better Results from AI Tools

AI tools are quickly becoming more than just smart assistants. They’re turning into reliable teammates we can count on. Whether it’s drafting blog content, summarizing long meetings, analyzing customer data, or even writing sales emails, these tools are helping us move faster and work smarter. But as powerful as they are, many users still feel disappointed with what they get out of AI. Why? Because they’re missing one key ingredient: How you talk to your AI.

This is where prompt engineering comes in. It’s the skill that determines whether your AI acts like a helpful colleague or gives you a “dumb” response that misses the mark. And no, you don’t need to be a coder or AI expert to get this right.

If you’re just joining us, make sure to check out our first blog in this series: Introduction to Prompting and Why It Matters, where we laid the groundwork for why clear, structured prompting is the key to unlocking true AI productivity.

Today, we’re going one level deeper. Let’s explore how prompt engineering works, how you can apply it to your business workflows, and how to grow this skill over time.

So, What Is Prompt Engineering?

Prompt engineering is the skill of crafting instructions that guide AI to give you exactly what you want.

To master this, you’ll need to learn how to:

• Give instructions that are direct and structured
• Adapt prompts for different AI tools and use cases
• Refine prompts over time to improve consistency and results

Let’s start by looking at how prompting actually happens in different workflows.

Manual Prompting vs. Programmatic Prompting

Before diving into advanced techniques, it’s helpful to understand the two broad ways prompts are used.

Manual Prompting

Most people start with manual prompting. Let’s say you’re using ChatGPT, Copilot in Word, or any other AI assistant. If you type your request directly into the interface, that’s manual prompting. This is the most common form of prompting used by business professionals. You type in a query or instruction manually, review the output, and if needed rephrase or refine it. It’s quick, accessible, and useful for everyday tasks like drafting an email, getting content ideas, or asking for a summary. It’s flexible and fast, and ideal for non- technical users.

The downside? It’s harder to scale. If you need to perform the same task every day, you’d have to manually re-enter your prompt each time.

Programmatic Prompting

Imagine you want your AI to generate and send a weekly sales summary email automatically. You’d build that using programmatic prompting. This involves using prompts as part of a system or application. That is your prompt is part of a backend workflow that runs regularly, often through an API or automation tool. This makes AI a consistent part of your process.  In these cases, prompts are written as part of a software flow. You’ll likely use prompt templates, call APIs, and integrate outputs with other systems. Programmatic prompting lets teams scale AI workflows and standardizes responses at speed.

Now you know which type of prompting you need in your workflow. Once you understand where you’ll be using prompts, the next step is learning how to craft better ones. That’s where tuning comes in.

Prompt tuning

Just like any business strategy or presentation, your prompt might not be perfect on the first try. That’s okay. The key is to treat your prompt like a living document, something you refine until you consistently get the best results.

Prompt tuning is the process of refining and optimizing your prompt over time to get better, more consistent outputs. In a technical sense, it can involve small adjustments (or even gradient-based fine-tuning of “soft” prompt tokens), but in business practice it often means iterating on wording, structure, and examples until you hit “repeatable success.”

Let’s look at an example.

Initial prompt:

“Summarize this product review.”

Tuned prompt:

“You are a product analyst. Summarize the following product review in 3 bullet points. Highlight the customer’s sentiment, any mentioned features, and suggestions for improvement.”

That little bit of structure and clarity makes a big difference.

A great prompt today can become a productivity hack tomorrow. Once you start tuning your prompts, you’ll notice patterns and that leads to better output, faster. By tracking which prompts deliver the highest-quality outputs, you can build a library your whole team can reuse and there won’t be a need to reinvent the wheel each time.

Practical Tips you can try:

• Log your iterations: Keep a simple spreadsheet of prompt versions and output quality notes.
• A/B test prompts: Try two variants side by side to see which yields better results.
• Share “champion” prompts: Build an internal prompt library so everyone benefits from proven templates.

Prompt Chaining

Sometimes your task is just too complex for a single prompt. That’s where prompt chaining shines. Prompt chaining is the process of breaking a complex task into smaller, connected prompts, where each output feeds into the next. This mirrors how humans tackle multi-part tasks: gather data, analyze, then summarize. Chaining gives you finer control over each sub-task, reduces confusion, and lets you insert checks or transformations between steps.

Example:

Prompt 1: Summarize a customer complaint.

Prompt 2: Generate a suitable response based on that summary.

Prompt 3: Rephrase the response to match your brand voice.

This method improves control, especially for tasks that require multiple steps, contextual adaptation, or a blend of reasoning and tone. But make sure to keep chains to 3–5 steps to avoid compounding errors. Prompt chaining is widely used in customer service bots, report generators, multi-step workflows, and agents that operate across different tools.

Prompt Template

Let’s say your sales team writes dozens of follow-up emails a week. Why reinvent the wheel every time?

With prompt templates, you define the structure once and reuse it with variables.

It is like a fill-in-the-blanks function. You define the structure of a prompt, and the dynamic parts (variables) are filled in with relevant input each time. Templates enforce consistency, speed up prompt creation, and allow non-technical users to plug in values without worrying about the exact phrasing. In enterprise environments, templates are often embedded directly into internal tools or used in AI-powered CRMs, and workflow automation systems.

Example Template:

“You are a sales rep. Write a follow-up email to {{Name}} at {{Company}}. Mention their interest in {{Topic}} and suggest a 15-minute call next week. Keep it short and friendly.”

Fill in the blanks, and you get high-quality, personalized emails instantly.

Priming

Priming is an initial instruction that sets the AI’s “persona” or operating mode for the rest of a session or chain. Imagine briefing a consultant before a project. That’s what priming does for your AI.

Example priming prompt:

“You are a senior marketing strategist. For this session, always use a confident, data-backed tone. Format all outputs in numbered lists and keep them under 200 words.”

Now, all your future prompts inherit this tone and structure.

This sets expectations and now you don’t have to re-explain yourself every time.

Priming is incredibly helpful when:

• You’re running long prompt sessions
• You want to ensure tone consistency
• You’re using AI to draft branded materials

Self-Consistency

AI sometimes gives slightly different answers each time. So instead of trusting the first response, try self-consistency: ask the same question multiple times and compare the answers.

Instead of one answer, you run the prompt five times, gather all responses, and select the answer that appears most often.

This essentially means more data = more confidence on the answer given. It reduces random hallucinations and improves reliability, especially in reasoning or complex analysis tasks.

Meta Prompting

Ever feel stuck trying to phrase your prompt just right? Ask the AI to help you. Meta-prompting is prompting the AI to help you improve your prompt. It’s like getting writing help from the AI before even asking your actual question.

Example:

“Improve this prompt for clarity and tone: ‘Write about our product.'”

Essentially, you’re using the model’s language capabilities to bootstrap better instructions. Meta-prompting is great for beginners and pros alike. It saves time, clarifies your intent, and improves output quality. This accelerates prompt development and helps you find the right tone and structure.

In conclusion, Prompt Engineering is a skill that scales

Prompt engineering isn’t about being perfect, it’s about being intentional. Every well-structured prompt brings you closer to scalable productivity with AI.

Let’s recap how these techniques connect and build on each other:

• You start with manual prompting, then grow into programmatic workflows.
• You refine your inputs through prompt tuning and templates.
• For complex tasks, you break it down using prompt chaining.
• You set context with priming, increase trust with self-consistency, and boost your skills using meta-prompting.

The more you practice, the more natural this becomes.

In our next post, we’ll take a closer look at what makes a great prompt—from structure to style to tone. Stay with us as we turn this must-have AI skill into a core part of your workflow.