3 Design Patterns for AI Workflows in 2025

Artificial intelligence (AI) has become a buzzword across industries, creating a whirlwind of discussions, innovations, and expectations. For marketing agencies looking to streamline operations and enhance productivity, understanding AI design patterns is crucial. These patterns can help cut through the noise and apply AI in a way that genuinely benefits workflows. In this article, we delve into four essential AI design patterns for 2025: LLM Augmentation, Prompt Chaining, and Workflow Routing.

Pattern 1: LLM Augmentation

The first pattern, LLM Augmentation, revolves around utilizing large language models (LLMs) to enhance existing systems. LLMs like GPT-4 can process and generate human-like text, which agencies can leverage for various tasks. This pattern involves integrating LLMs to augment marketing agency processes such as content creation, customer interaction, and data analysis.

Use Cases of LLM Augmentation

• Content Creation: Automate the generation of social media posts about your company’s latest blog post. LLMs can produce creative content that resonates with audiences and aligns with brand voice.
• Customer Interaction: Implement chatbots powered by LLMs to handle customer queries. This ensures prompt responses and frees up human resources for more complex tasks.
• Data Analysis: Use LLMs to analyze large datasets and extract meaningful insights, enabling data-driven decision-making. 

By augmenting workflows with LLMs, agencies can enhance efficiency and focus more on strategic initiatives rather than repetitive tasks.

Pattern 2: Prompt Chaining

Prompt Chaining is a powerful pattern that involves breaking down complex tasks into smaller, manageable prompts. This pattern is particularly valuable for marketing agencies when developing campaigns or automating agency tasks.

How Prompt Chaining Works

• Task Breakdown: Decompose large tasks into a series of smaller prompts. For example, a campaign strategy can be broken into prompts for research, audience analysis, message crafting, and channel selection.
• Iterative Improvement: Each prompt is refined based on the output of the previous one, ensuring a coherent and comprehensive final product.
• Flexibility: Allows for adjustments and refinements at each stage, leading to optimized outcomes.

Prompt Chaining ensures that workflows remain flexible and adaptable, allowing agencies to tailor processes to specific client needs and goals.

Pattern 3: Workflow Routing

Workflow Routing is about directing tasks to the appropriate AI models or human agents based on complexity and context. This pattern ensures that tasks are handled efficiently and effectively within marketing agency processes.

Example Where Workflow Routing Can Help Out

Let’s take the example of monthly reports. Of course, there is a strong human element required and we can’t replace an account manager with an AI and expect to keep clients long term. 

But, we can make sure that the human element is spent doing the important work. Here’s an example of a reporting workflow that is partially automated to help out during those painful first few days of the month: 

1. Data from platforms: Use tools or AI to get data from the platform into your reporting document
2. Use a prompt to check the accuracy of that data (important, AI can hallucinate, but you can have another AI check its work against your data source-of-truth). 
3. Use a workflow route to make decisionssome text
   1. For clients whose report is generally positive, have a tool write the report out, check it for errors, and assign it for human review by a junior team member. 
   2. For clients where results were negative, the report should be assigned out to write by a senior member of the team. 
4. One last double-check before sending these out to your clients. 

Tips: 

• For pure data, have it be inserted by a tool directly (e.g. spreadsheet) from your data source rather than routing it through an LLM. This avoids issues. 
• If your reports are highly highly customized, this might not make sense for your agency. That said, if you’re putting in numbers manually at all you can at least solve that. This way, your account manager’s time is spent on what they’re good at rather than a menial task. 

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A Note on Prompts

Prompt engineering is a bit difficult. I think we’ve all asked the same question to ChatGPT  twice and received wildly different answers. 

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If you take nothing else from this blog post, have it be this: 

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To get something good out of an LLM, you must provide it with a few good examples of what you want out of it. 

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The regular ChatGPT “chat’ model has some limited support for this (just put your examples in your ask prompt), but you will get much more consistent results if you’re using the API of these models. Specifically, the prompts for system, assistant, and user. 

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Overview Of LLM Prompt Types

• System: The system prompt is usually short, you’re just telling the LLM what its role is. I like “You’re an intelligent, helpful, writing assistant” or “you pass butter” (ifkyk)
• Assistant & User: This is where the fun begins. Here, you can alternate giving the model a user prompt, and an example of expected output for the assistant. You can do this a few times. Finally, you provide your real user prompt. 

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An example outline of a good LLM prompt would be

• System, you’re an intelligent, helpful writing assistant. 
• User: your prompt with an example input
• Assistant: example 1
• User: your prompt with another example input
• Assistant: example 2
• User: your prompt with the actual input

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From here, given good examples (you might have to play around with these a bit), your LLM will behave more consistently. It’s almost like you’ve trained it to do a specific task rather than allowed it to go crazy. 

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One More Thing: Most models have a “temperature” control, usually a number between 0 and 1. Experiment a bit with this. 1 usually means you’re allowing it to be creative and think outside the box. I find the best balance for “tool” tasks to be something like 0.7. This allows it to take varied input but still usually outputs something consistent. Test a few prompts here. 

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Conclusion

In 2025, understanding and implementing AI design patterns will be key for agencies aiming to achieve their goals with AI. Each pattern offers distinct advantages that streamline agency automation and improve processes, leading to enhanced productivity and better client outcomes.

Knowing how to use the right tool for a given job or workflow is crucial in today's fast-paced digital landscape. By leveraging these AI design patterns, agencies can cut through the AI buzz and harness its true potential to drive success. 💡

These patterns are not just theoretical concepts; they are practical tools that, when applied correctly, can transform how marketing agencies operate and thrive in an ever-evolving industry. So, gear up and integrate these patterns into your workflows to stay ahead of the competition in 2025 and beyond! 🚀

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