What is an Ai Harness?

This is a summary of a video I published on YouTube. You can click on the link below to watch the full video.

The mistake developers are making with AI

Most developers are treating AI models like magic boxes. They throw in a big prompt, hope for a clean result, and then wonder why the output is inconsistent, wrong, or just plain weird.

That approach will waste your time.

The problem isn’t the model. It’s how you’re using it.

The model is not the product

GPT, Claude, Gemini—these are just engines. By themselves, they’re unpredictable. They hallucinate, drift, and behave differently from one version to the next.

The real value comes from the harness you build around the model.

A harness is everything that shapes the model’s behavior: prompts, rules, workflows, integrations, and the sequence of steps you design. Without that structure, you’re just poking at an API and hoping for the best.

Think like a developer, not a prompt gambler.

Break the work into steps or expect failure

The biggest shift you need to make is this: stop trying to get one prompt to do everything.

That’s where most AI workflows fall apart.

Instead, break the task into smaller, controlled steps. Each step does one job well. Then you chain them together.

I’ll give you a simple example from my own workflow:

• Generate and clean a transcript using a specialized service
• Run a second pass to refine the text
• Run a final pass to format it into a structured article

Each step has its own instructions and expectations. The result? Output that’s about 98% usable.

Try doing that in one prompt, and it falls apart quickly.

This is just software engineering, applied properly

If you’ve been around development for a while, none of this is new. It’s separation of concerns.

One component handles input. Another transforms it. Another formats it. You orchestrate the flow.

Same idea, different tools.

Tools like Zapier can act as the coordinator, moving data between steps. You don’t need to build everything from scratch, and you probably shouldn’t.

Models change—your thinking shouldn’t

Here’s another trap: getting attached to a specific model.

They change constantly. Even within the same provider, updates can break your workflow. I’ve had systems stop working properly just because a model version changed behavior.

If your approach depends on one model acting a certain way, you’re building on sand.

What holds up over time is your ability to design the harness: choosing the right model for each step, adjusting prompts, and restructuring workflows when needed.

The real opportunity

You’re probably not going to build your own AI model. That’s not where the opportunity is.

The opportunity is in designing systems that use these models effectively.

That means understanding workflows, APIs, data flow, and how to break problems into parts that machines can handle reliably.

If you focus on that, you’re building something durable. If you don’t, you’ll spend your time chasing prompts that never quite work.

Watch the video on YouTube here 👉 Ai Harness

Thanks for reading!
Stef