webinar – yannick-design.de

A Step-by-Step Guide to Gen AI Workflows 🇬🇧

yannick-design.de — Fri, 04 Jul 2025 12:50:28 +0000

A Step-by-Step Guide to Gen AI Workflows

Watch the full video here

Quick background about me:
I recently tackled a common challenge in our organization – creating comprehensive surveys that actually generate actionable insights. What traditionally took days of expert work can now be accomplished in 20-30 minutes with a well-designed Gen AI workflow, I’d say.

In this post, I’ll walk you through how to build effective AI workflows step by step, using my survey creation workflow as a practical example. Whether you’re new to AI workflows or looking to improve your approach – these principles will help you design processes that deliver consistent, high-quality results.

Watch the full video here

Why Build AI Workflows?

Before diving into the „how,“ let’s talk about the „why.“ This is actually easier than you might think. A well-designed AI workflow can actually save significant time on repetitive tasks, ensures consistency across outputs, and above all makes expertise more accessible to your entire team.

What this means is, it breaks complex tasks into manageable steps and creates reusable processes that improve over time. In my survey example, we transformed what was once a specialized skill – requiring deep knowledge of survey design principles – into a streamlined process anyone on the team can follow.

Step 1:
Interview an Expert

The first step in creating any effective workflow is understanding what experts currently do to accomplish the task. This has to do with the fact that you need to capture their thought process, not just their output.

Here’s a list of excellent questions to ask an expert – I personally use these relatively frequently:

What exactly is the goal of the workflow?
What does the result/target artifact(s) need to include?
What information did you need in order to start the task?
What was your thought process from beginning to end?
How did you evaluate the quality of your sources, data and intermediary steps?

Also, let me say upfront: Even a quick 15-30 minute conversation with a knowledgeable colleague can save you hours of frustration later and help align your workflow with what’s actually needed.

For my survey workflow, I spoke with our research team to understand their process for designing surveys that yield meaningful insights. This conversation revealed crucial requirements: surveys need to be engaging but brief, questions must connect to clear hypotheses, and above all – analysis needs to be planned before deployment.

Step 2:
Define the Goal

Every effective workflow needs a concrete goal focused on creating a specific artifact that solves a problem. This means you can’t just say „help teams understand their customers better“ – that’s too abstract.

For the survey workflow, I defined two clear artifacts as goals:

A finished survey with all sections, questions, and explanatory text
An analysis and interpretation guide to help make sense of the collected data

Notice how these are tangible outputs rather than abstract goals. This is particularly important because it makes it easier to evaluate success and iterate on your workflow.

Step 3:
Craft Your Workflow

With your goal defined, break down the process into logical steps. Think about what information is needed at each stage (inputs), what is produced at each stage (outputs), and what tools will be used.

For my survey workflow, I identified these key steps:

Define the research question
Expand the main question into sub-questions
Draft hypotheses for each sub-question
Select the most relevant hypotheses to test
Draft survey questions based on selected hypotheses
Generate the complete survey with all sections
Critique the survey and improve it
Create an analysis and interpretation guide

Each step has clear inputs and outputs. What this means is, the „Expand Research Question“ step takes the main research question and research context as inputs and produces expanded sub-questions as output.

Step 4:
Design Your Prompts

This is where the magic happens, I’d say. Well-crafted prompts are above all the building blocks of effective AI workflows.

An effective prompt should follow this structure:

Introduction: Tell the AI its role and main task
Context Section: Provide necessary background information
Detailing Section: Reinforce and detail the task after providing context
Closing: Include language constraints and final guidance

Let’s examine the prompt from the „Expand Research Question“ step:

You are a Research Assistant and need to expand the Main Research Question into Sub-Questions that help explore smaller nuances of the initial question.

Context of Research:
„““{{Research Context}}“““

Main Research Question:
„““{{Research Question}}“““

Make a List starting with the Main Research Question and generated Sub-Questions. Ensure to mark each clearly.

Make it {{Language}}. Let’s Think Step by Step:

This prompt defines the AI’s role as a Research Assistant, provides context through variables that can be easily replaced, gives clear instructions on the expected output format, and ends with language specification and the „Let’s think step by step“ technique to improve quality.

I personally recommend: Always use the largest models available first – for LLMs this means Reasoning Models like Claude 3.5 Sonnet or GPT-4. These models „think“ before answering and are able to produce more complex and more reliable output in one single step than other types of LLMs.

Let’s examine the prompt from the „Expand Research Question“ step:

You are a Research Assistant and need to expand the Main Research Question into Sub-Questions that help explore smaller nuances of the initial question.

Context of Research:
„““{{Research Context}}“““

Main Research Question:
„““{{Research Question}}“““

Make a List starting with the Main Research Question and generated Sub-Questions. Ensure to mark each clearly.

Make it {{Language}}. Let’s Think Step by Step:

Step 5:
Choose a Test Case

Before implementing your entire workflow, select a realistic test case. This is actually more important than you might think.

For my survey workflow, I chose to create an employee happiness survey for our digital agency, which has a high proportion of senior roles and few junior positions. I also made a little documentation of all available questions and fields in Microsoft Forms to use this documentation as input for the survey drafting step.

This specific context gave me the constraints needed to properly test the workflow.

Step 6:
Start Testing

When testing your workflow, follow these principles:

Test early and focus on one prompt at a time
Iterate on each prompt until you’re satisfied before moving to the next
Identify and fix issues immediately rather than testing the entire workflow at once

This has particularly to do with the fact that nothing is more frustrating than a workflow that doesn’t work and you have no idea why it doesn’t work.

In my testing, I discovered that the prompt for drafting hypotheses needed refinement to generate better connections to the research questions. By fixing this single prompt before moving on, I avoided compounding errors further down the workflow.

Step 7:
Document Steps with Clear Input-Output Relationships

For each step in your workflow, clearly document:

Description: A brief explanation of what the step accomplishes
Team or Person responsible: Who executes this step
Time: Estimated duration to complete
Inputs: What information is needed
Outputs: What this step produces
Blockers & Bottlenecks: Potential challenges
Tools: AI models or platforms used
Prompt: The exact prompt text with variables clearly marked
Cost: Resource requirements (time, subscription fees, etc.)

This documentation makes your workflow transparent, shareable, and improvable. What this means is, team members can understand how the pieces fit together and can actually contribute to improving the process.

Step 8:
Refine Based on Results

Once you’ve tested individual prompts, run through the complete workflow and evaluate the final output against your goal.

For my survey workflow, I compared the generated survey with professional surveys our research team had created previously. I looked for question clarity, logical flow between sections, and above all whether the analysis guide would produce actionable insights.

Step 9:
Organize for Future Use

Once your workflow delivers satisfactory results, organize it for easy access and future use:

Keep your documentation clean and updated
Mark which steps have been tested successfully
Consider taking breaks between workflow development sessions
Resist the urge to automate too early – ensure the workflow is stable first

Let me say upfront: If you keep your board organized and clean, you will be able to keep going way faster when you come back to it.

Common Challenges
and Solutions

Challenge: Prompts aren’t producing consistent results
Solution: Add more structure to your prompts and be specific about the format you want. Use variables for customization while keeping the core instruction consistent.

Challenge: Workflow steps don’t connect smoothly
Solution: Ensure outputs from one step match the expected inputs for the next. What this means is, sometimes you’ll need to add intermediate steps or refine prompts.

Challenge: The final output doesn’t meet quality standards
Solution: Work backward to identify which step is introducing issues. Sometimes adding a critique step can help improve quality.

Real-World Impact

The Survey Workflow in Action

Let’s see how this all works in practice with our survey workflow example:

We begin with a clear research question: „How happy are people working at the company?“

This expands into sub-questions covering areas like work environment, project allocation, leadership, career development, and more. Hypotheses are drafted for each area, such as „Senior employees feel their specialized skills are underutilized.“

The most relevant hypotheses are selected based on the research goals, survey questions are drafted to test these hypotheses, and a complete survey is generated with proper sections, including demographics, overall happiness assessment, workload questions, leadership evaluations, etc.

The result: A comprehensive employee happiness survey that would typically take days to create but now takes under 30 minutes.

Ready to build your own AI workflow?

Start small:

Choose a repetitive task you know well
Talk to others who perform this task regularly
Define a clear artifact as your goal
Map out the logical steps
Create prompts for each step, testing each step with a specific example
Refine until satisfied
Document for future use

Remember that workflow design is a skill that improves with practice. Some people are really good at workflow design, but with any task – some people like it and some don’t. It is perfectly fine if you struggle with workflow design. You don’t have to construct workflows – this skill is not inherently more valuable than any other skill

Conclusion

Building effective Gen AI workflows isn’t about complex technical skills – it’s about clearly defining goals, breaking down processes into logical steps, and writing prompts that guide AI to produce what you need.

What this means is, by following the steps outlined in this post, you can transform time-intensive tasks – like creating comprehensive surveys – into efficient, repeatable processes. Your workflows might start simple, but they’ll grow in sophistication as you learn what works.

Remember to:

Start with expert input
Define concrete goals
Break processes into discrete steps
Test early and often
Document thoroughly & organize for reuse

Above all:
Resist the urge to automate too quickly. Try to resist automating the workflow as much as possible – naturally it’s going to change over time and with automation there comes a whole layer of complexity that just makes it harder.

What task will you transform with your first Gen AI workflow? If you have ideas, problems, fears, thoughts, or use cases you want to solve concerning the use of GenAI tools, please feel free to reach out.

Wanna talk about what that means for your company?

Let’s discuss how this proven framework can address your organization’s specific challenges and opportunities.

The Future of the Web: The Impact of Generative AI 🇬🇧

yannick-design.de — Fri, 04 Jul 2025 11:51:54 +0000

The Future of Web

How Generative AI is Actually Reshaping Everything

Watch the hole webinar here

A quick background on me – I’ve been working with AI systems for quite some time now, and I have to say, what we’re seeing with generative AI is something that’s fundamentally changing how we think about web interaction. Let me break this down for you in a way that’s actually practical.

Long-term Changes

The AI Assistant Era is Coming – Whether We Like It or Not

So here’s the thing – and I say this based on what I’m seeing in the industry – AI assistants are going to become the primary interface for user interaction within the next three to five years. That’s not just speculation, that’s what the data is telling us.

I mean, think about it: 62 percent of users already prefer chatbots over traditional support. And here’s what’s really interesting – 71 percent of Gen Z users are using chatbots for product research. That’s not a small shift, that’s a fundamental change in user behavior.

What does this mean practically? Well, I think we’re looking at a situation where websites become essentially databases that AI systems query, rather than interactive user interfaces. Microsoft, Google, Apple – they’re all integrating these assistants deep into their devices, using both local and cloud-based models to provide personalized, contextual support.

The key thing here is that these aren’t just fancy chatbots anymore. We’re talking about sophisticated RAG systems – that’s Retrieval-Augmented Generation for those who aren’t familiar – that can actually understand context and deliver precise information.

Watch the hole webinar here

Medium-term Perspective

The Hybrid Era – And Why It’s More Complex Than You Think

Now, in the medium term – and this is where it gets interesting – I believe we’re going to see a hybrid usage pattern. Some users will still prefer traditional browsing, while others will rely primarily on AI assistants to get specific information.

Here’s what I’m seeing: user expectations are changing rapidly. They’re becoming more impatient and demanding immediate, precise answers. That means companies need to respond by personalizing their content more heavily and understanding their target audiences better.

But here’s the challenge – and this is something I tell my clients all the time – you need to make your content machine-readable and semantically rich. That’s not just about SEO anymore, that’s about being discoverable by AI systems. And yes, you can automate a lot of this with LLMs, but you still need the strategic thinking behind it.

The user intent becomes crucial here. We’re not just talking about keywords anymore, we’re talking about understanding the underlying motivations and contexts of user queries.

Short-term Actions

What You Should Actually Be Doing Right Now

Okay, so practically speaking – and this is where I always get concrete with my recommendations – companies should start implementing chatbots on their websites immediately. Not because it’s trendy, but because it gives you qualitative insights into user preferences and optimizes your content production.

Here’s what I recommend: Start with prototypes. Run design sprints. Test the functionality and value of AI assistants with your actual users. You need an interdisciplinary team – content experts, product specialists, support staff – because this isn’t just a tech project.

And here’s the thing that many companies struggle with: you need to be willing to give up control and let the systems operate independently. That requires courage and the acceptance that mistakes will be made, but those mistakes are valuable learning opportunities.

A Pragmatic Approach

From Theory to Implementation

Look, I’ve seen too many companies get stuck in the theoretical phase. What I always tell my clients is: start with comprehensive knowledge about your products and services, then structure that data for AI systems. This enables precise and contextually relevant answers.

The key is understanding that we’re not just building chatbots, we’re building intelligent systems that can actually understand and respond to complex user needs. That’s a fundamental shift in how we think about user experience.

My Take on the Future

What This Actually Means

Here’s my honest assessment: The future of the web lies in the harmonious integration of technology and human interaction. Generative AI isn’t just changing user behavior – it’s fundamentally altering how companies communicate with their customers.

But here’s what I think is most important: success lies in the ability to adapt and use technological possibilities creatively and thoughtfully. We’re not just implementing new tools, we’re reimagining the entire interaction paradigm.

As someone who’s been working in this space for a while, I can tell you that the companies that succeed will be those that embrace this change proactively rather than reactively. The technology is there, the user behavior is shifting – the question is whether businesses are ready to meet their customers where they’re going, not where they’ve been.

This analysis is based on current industry trends and practical implementation experience. The timeline and specific impacts may vary depending on adoption rates and technological developments.

Wanna talk about what that means for your company?

Let’s discuss how this proven framework can address your organization’s specific challenges and opportunities.

webinar – yannick-design.de

A Step-by-Step Guide to Gen AI Workflows 🇬🇧

A Step-by-Step Guide to Gen AI Workflows

Why Build AI Workflows?

Step 1:
Interview an Expert

Step 2:
Define the Goal

Step 3:
Craft Your Workflow

Step 4:
Design Your Prompts

Step 5:
Choose a Test Case

Step 6:
Start Testing

Step 7:
Document Steps with Clear Input-Output Relationships

Step 8:
Refine Based on Results

Step 9:
Organize for Future Use

Common Challenges
and Solutions

Real-World Impact

Ready to build your own AI workflow?

Conclusion

Wanna talk about what that means for your company?

More stuff to read

The Future of the Web: The Impact of Generative AI 🇬🇧

The Future of Web

Long-term Changes

Medium-term Perspective

Short-term Actions

A Pragmatic Approach

My Take on the Future

Wanna talk about what that means for your company?

More stuff to read

webinar – yannick-design.de

A Step-by-Step Guide to Gen AI Workflows​ 🇬🇧

A Step-by-Step Guide to Gen AI Workflows

Why Build AI Workflows?

Step 1: Interview an Expert

Step 2: Define the Goal

Step 3: Craft Your Workflow

Step 4: Design Your Prompts

Step 5: Choose a Test Case

Step 6: Start Testing

Step 7: Document Steps with Clear Input-Output Relationships

Step 8: Refine Based on Results

Step 9: Organize for Future Use

Common Challenges and Solutions

Real-World Impact

Ready to build your own AI workflow?

Conclusion

Wanna talk about what that means for your company?

More stuff to read

The Future of the Web: The Impact of Generative AI 🇬🇧

The Future of Web

Long-term Changes

Medium-term Perspective

Short-term Actions

A Pragmatic Approach

My Take on the Future

Wanna talk about what that means for your company?

More stuff to read

A Step-by-Step Guide to Gen AI Workflows 🇬🇧

Step 1:
Interview an Expert

Step 2:
Define the Goal

Step 3:
Craft Your Workflow

Step 4:
Design Your Prompts

Step 5:
Choose a Test Case

Step 6:
Start Testing

Step 7:
Document Steps with Clear Input-Output Relationships

Step 8:
Refine Based on Results

Step 9:
Organize for Future Use

Common Challenges
and Solutions