Designing Simple Chatbots

Lesson 11: From Prompts to Conversational Agents

One of the most accessible ways to leverage your structured prompts is through chatbots - conversational agents designed to perform specific tasks through natural dialogue.

The prompt library you've developed in our previous lesson serves as the foundation for creating more sophisticated AI applications.

That’s why it is important to master structured prompting … not so you can get this one AI interaction to work … but so you can builder better and bigger AI content systems.

What makes these custom chatbots powerful isn't just the technology—it's the specialized focus on specific, high-value tasks.

Why consider creating a chatbot? The applications are remarkably versatile across different professional contexts:

For content creators, chatbots can serve as specialized assistants that streamline repetitive aspects of your workflow.

Imagine a chatbot that helps you brainstorm newsletter topics based on your audience analytics, transforms your long-form content into social media posts tailored to different platforms, or analyzes your writing for clarity and engagement. Rather than switching between multiple tools, a well-designed chatbot brings these capabilities into a conversational interface that fits naturally into your creative process.

Educators can leverage chatbots to extend their teaching presence beyond class hours.

A chatbot could provide personalized feedback on student drafts using your specific rubric, create differentiated practice activities based on individual learning needs, or even simulate discussion scenarios for students to practice critical thinking. These applications don't replace the educator—they amplify your ability to provide timely, consistent support to students.

For technical writers, chatbots can become powerful quality control tools that help maintain documentation standards.

A specialized bot might analyze technical content for reading level and jargon, verify that procedures follow your organization's required structure, or even generate template-based documentation for common scenarios. These applications help technical writers focus on high-value communication challenges while ensuring consistency across large documentation sets.

What makes these applications powerful isn't just the technology—it's the specialized focus on specific, high-value tasks. This brings us to our first crucial insight about effective chatbot design.

Understanding Chatbots as Tools, Not Personas

When designing chatbots, the most common mistake is creating a persona rather than a tool. People often approach chatbot design by thinking, "I want to create a writing coach" or "I need a research assistant." This approach seems intuitive but often leads to disappointing results, because you are leaving too much to chance … or the models default training.

Instead, think of your chatbot as a specialized tool designed to perform a specific task or set of closely related tasks. Rather than creating a "content marketing assistant," consider creating a "headline optimizer" or a "content repurposing guide" that excels at one clearly defined function.

This distinction between tools and personas is crucial for several reasons:

1. Focus creates effectiveness: The more narrowly defined a chatbot's purpose, the more effectively it can perform its specific function. A chatbot designed to "help with writing" faces an impossibly broad domain, while one designed to "identify passive voice and suggest active alternatives" can be finely tuned to excel at that specific task.

2. Clear user expectations: Users understand exactly what the chatbot can and cannot do, leading to more satisfying interactions. When expectations match capabilities, users experience the chatbot as helpful rather than frustrating.

3. Reduced complexity: Designing for a specific task simplifies the development process and reduces the likelihood of errors. Each additional capability introduces new edge cases and potential issues; focusing on a single capability keeps the design manageable.

4. Better measurement: It's easier to evaluate and improve a chatbot's performance when its purpose is clearly defined. With a narrow focus, you can create specific success metrics and test cases that would be impossible for a general-purpose assistant.

Consider the difference between these two approaches:

General Approach: "I'll create a social media assistant chatbot that helps with all aspects of social media management."

Focused Approach: "I'll create a chatbot that helps transform long-form blog content into engaging Twitter threads while preserving key points and maintaining brand voice."

The general approach sounds appealing but presents enormous challenges. How would you train it to handle every aspect of social media? How would users know what to ask? How would you measure success?

The focused approach, by contrast, defines a clear problem with specific inputs, outputs, and success criteria. You can design prompts specifically for analyzing blog content, identifying key points, and reformatting those points as Twitter-appropriate content. Users know exactly what to expect, and you can measure success by how effectively the chatbot preserves key information while adapting to the platform's constraints.

Remember, AI excels when properly constrained. By limiting your chatbot's scope, you're not reducing its value—you're enhancing it. The most useful tools in your workshop aren't the multi-purpose ones—they're the specialized tools designed to excel at specific tasks.

This tool-oriented mindset also helps avoid one of the most common pitfalls in AI interaction: the illusion of general intelligence.

When users perceive a chatbot as a general assistant (a persona), they develop unrealistic expectations about its capabilities. When the chatbot inevitably fails to meet these expectations, users become frustrated and distrustful.

By presenting your chatbot as a specialized tool with clear boundaries, you set appropriate expectations from the start.

The Anatomy of an Effective Chatbot

When we look at chatbot design as technical writers, educators, or content creators, we're really looking at a new form of communication. Just as we carefully craft our documents, lesson plans, or content strategies, we need to thoughtfully design our chatbots to serve our audiences effectively.

Every effective chatbot consists of three core components that mirror what we already know about good communication:

1. Purpose: A clear definition of what the chatbot does and doesn't do

2. Process: A structured approach to accomplishing its task

3. Presentation: How the chatbot communicates with users

Let's explore each of these components in a way that connects to our everyday work.

Defining Your Chatbot's Purpose

As content professionals, we know that defining the purpose is always the first step in any project. For your chatbot, this means identifying the specific problem it solves.

I've found that the most useful chatbots address very specific needs rather than trying to be all-purpose assistants. For example:

• For content creators: "This chatbot helps transform long-form blog posts into engaging Twitter threads while preserving key points."

• For educators: "This chatbot helps students identify credible sources for their research papers by analyzing citation information."

• For technical writers: "This chatbot simplifies complex procedures by breaking them down into step-by-step instructions with visual cues."

Notice how each purpose identifies both the user and the specific task. This clarity is something we strive for in all our communication, and it's equally important in chatbot design.

When defining your purpose, ask yourself questions that connect to your professional practice:

• Who specifically will use this chatbot? (Just as you would define your audience for any content)

• What particular problem are they trying to solve? (Similar to defining the scope of a document)

• What falls outside the chatbot's capabilities? (Like setting boundaries in a content brief)

• How will users know if the chatbot has successfully helped them? (Similar to defining learning objectives or content goals)

Document these answers clearly. In my experience working with various content teams, this documentation becomes invaluable as your project evolves—just like a good creative brief or project plan.

Designing Your Chatbot's Process

The process component describes how your chatbot will accomplish its purpose—the conversation flow that guides users from question to answer.

This is where your structured prompt blocks become especially valuable. When I've designed chatbots for my own work, I've found that a good process typically follows these steps:

1. Initial orientation: Introducing what the chatbot does and setting expectations

2. Information gathering: Collecting necessary inputs from the user

3. Processing: Applying your prompt blocks to transform inputs into outputs

4. Output delivery: Providing results in a useful format

5. Refinement: Allowing for feedback and adjustments

For each step, you'll need to design appropriate prompt blocks. For instance, if I were creating a chatbot to help transform blog content into social media posts, I might include:

• A welcome message that explains what the chatbot does and what I'll need to provide

• Questions about my target platform and audience (since what works on LinkedIn differs from Twitter)

• Analysis of my blog content to identify key points

• Generation of platform-appropriate content that maintains my voice

• Options to refine the generated content based on my feedback

These prompt blocks can come directly from your prompt library, combined in sequences that accomplish your chatbot's specific purpose.

What's fascinating to me is how this process mirrors good instructional design or technical writing. We're essentially creating a conversation that guides users through a process, helping them achieve their goals through well-structured steps—much like a well-designed tutorial or lesson plan.

Crafting Your Chatbot's Presentation

As content professionals, we know that how we say something is often as important as what we say. The presentation component addresses how your chatbot communicates with users. This includes:

• Tone and voice: How formal or casual should the chatbot be?

• Interaction style: Should the chatbot ask multiple questions or process everything at once?

• Error handling: How should the chatbot respond when it doesn't understand or can't complete a task?

• Visual elements: What formatting will make the chatbot's outputs most useful?

Your presentation choices should align with your users' expectations and the context in which they'll use the chatbot. In my work with educational technology, I've found that a chatbot for students might use a more casual, encouraging tone, while a chatbot for professional researchers might prioritize precision and efficiency.

This is no different from how we adapt our writing style for different documentation types or audience needs. Just as we might use a friendly, conversational tone for a blog post but a more formal approach for technical documentation, our chatbots need to communicate in ways that feel appropriate to their context and users.

When designing my first chatbot to help with student feedback, I found that building in friendly encouragement along with specific guidance made the experience feel more supportive—similar to how I might balance critique with encouragement in face-to-face feedback sessions.

By thoughtfully designing these three components—purpose, process, and presentation—you create conversations that not only accomplish tasks but do so in a way that feels natural and helpful to users. The most effective chatbots don't just process information; they guide users through tasks in a clear, contextually appropriate manner that respects their needs and time constraints.

This approach to chatbot design draws on everything we already know about good communication—clarity of purpose, well-structured process, and appropriate presentation. We're simply applying these principles to a new medium of conversation.

The Connection to Rhetoric

What we're doing when we design chatbots isn't entirely new. In fact, we're drawing on principles that date back thousands of years to the ancient art of rhetoric—the study of effective communication and persuasion.

When I first began exploring AI, I was struck by how much chatbot design resembles classical rhetorical principles. The purpose component mirrors what rhetoricians call "exigence"—the situation that calls for a response. The process component resembles "arrangement"—how arguments are structured for maximum impact. And the presentation component connects to "style" and "delivery"—how messages are expressed and conveyed.

This connection isn't just academic—it's practical. For thousands of years, humans have been studying how to communicate effectively, developing frameworks that help us organize our thoughts, present information clearly, and connect with audiences. Now we're applying these same timeless principles to a new medium.

As content professionals, we're already practicing rhetoric every day, whether we use that term or not. When we structure a document for clarity, choose words carefully for our audience, or design learning experiences that guide students step by step, we're using rhetorical techniques.

Chatbot design simply extends these skills into conversational interfaces. Instead of arranging information on a page, we're arranging it in a dialogue. Instead of choosing a writing style, we're designing a conversational style. The medium has changed, but the fundamental principles remain the same.

Understanding this connection can help us leverage what we already know about effective communication as we venture into this new territory. Our experience with audience analysis, information design, and clear communication gives us a head start in creating effective chatbots.

Planning Your Chatbot: A Step-by-Step Approach

Now that we understand the core components of effective chatbot design, let's explore a systematic approach to planning your chatbot. I'll walk you through each step of the process while sharing how I applied these principles to create an educational feedback chatbot for my writing courses.