Part 4 - Keeping Humans in the Loop: A Smarter Approach to Gen AI

Here’s why this collaborative approach has become a cornerstone of how we use Gen AI—and how it can work for you.

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This is the fourth in a series of blog posts about How to build with GenAI- From strategy to implementation. In this series, we will explore the following questions:

  1. Is GenAI the right strategy for your product roadmap?
  2. Should you build or buy your GenAI model?
  3. How do you navigate the complexity of data to deliver clear results?
  4. Should you take a Human-in-the-loop approach?
  5. How do you manage costs while developing with GenAI?

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Gen AI has incredible power to simplify complexity, as we discussed in our last post. But no matter how advanced the model, it’s not infallible. It can struggle with ambiguity, miss important nuances, or fail to address unique edge cases.

At Yotascale, we quickly realized that the best Gen AI systems don’t just answer questions—they spark a dialogue. By keeping humans in the loop, we ensure that AI outputs are fine-tuned with human judgment, delivering smarter and more trustworthy solutions.

As Jeff Harris, our Director of Strategy and Operations, explains: “Gen AI is incredibly powerful, but it’s not always precise. Keeping humans in the loop ensures the right judgment is applied where it matters most.”

Here’s why this collaborative approach has become a cornerstone of how we use Gen AI—and how it can work for you.

Why Humans Are Essential in Gen AI Workflows

Even the most sophisticated AI models have limitations. They can confidently deliver answers that are incomplete or miss the context that a human would naturally consider. For instance, when Yotascale’s AI assistant started grouping cost drivers by tag, it did an impressive job—but it wasn’t perfect.

Jim Meyer, our VP of Engineering, shares: “Our AI might suggest grouping certain items, but it’s the user’s input that makes the results truly valuable. It’s a partnership, not a replacement.”

This human-in-the-loop approach isn’t just about improving accuracy—it’s about redefining how people interact with technology. “Chat-based Gen AI,” Jim adds, “is as revolutionary for user experience as the graphical user interface was in the ’80s and ’90s. It’s not just another tool; it’s a whole new way for people to engage with complex systems.”

By keeping humans in the loop, we’re ensuring that this new UX paradigm is as intuitive, flexible, and empowering as possible.

Real-World Applications of Human-AI Collaboration

At Yotascale, we’ve integrated human feedback into our Gen AI workflows in meaningful ways:

  • Confirming Groupings: When the AI suggests grouping cloud cost drivers or creating filters, it asks users to approve or modify its recommendations. This approach speeds up workflows without sacrificing accuracy.
  • Improving Recommendations: If the AI misinterprets a query or provides incomplete results, users can adjust the input or refine the context, creating a feedback loop that improves both the AI’s understanding and the user experience.
  • Ensuring Role-Based Relevance: By leveraging human judgment, we can align outputs with organizational structures, such as tailoring results to the priorities of a specific team or department.

As Jeff puts it: “It’s not just about getting the answer right—it’s about empowering users to get to the best outcomes more efficiently.”

The Benefits of Collaboration: Smarter, More Trusted AI

When humans and AI work together, the results go beyond just improving accuracy. Here’s what we’ve seen:

  1. Improved Decision-Making: By validating AI outputs, users can confidently make decisions based on reliable data.
  2. Faster Learning: Every interaction adds to the AI’s understanding, creating a continuous improvement cycle.
  3. Enhanced Trust: Users trust the system more when they feel in control and see how their input refines the results.

This collaboration transforms AI from a transactional tool into a trusted partner for solving complex problems.

Looking Ahead

Human-AI collaboration isn’t just about better results—it’s about reshaping the way we work together. By empowering users to guide and refine AI outputs, we’re building systems that are smarter, more flexible, and ultimately more human.

In our next post, we’ll explore what it takes to manage Gen AI responsibly, from cost controls to governance and compliance.

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