Recently, we’ve all been thrown into a crash course in prompting AI, learning how to ask chatbots the right questions to get the output we want. But there’s a gap between casual prompting and integrating an LLM into a production-grade implementation.
That’s where Prompt Engineers come in. They specialize in crafting effective inputs, known as “prompts”, to get the best outputs from AI systems; creating structured prompts that maximize accuracy, maintain safety standards, and function reliably across diverse use cases.
But like any software component, prompts need rigorous testing to ensure they perform as expected. This has led to the rise of a new and increasingly important role in the AI ecosystem: the Prompt Engineer Tester.
Related reading: Ten Reasons Your Technical Team Will Keep Growing – Despite AI
Prompt Testers Look at the Whole System
This role is more than checking if a prompt works, it’s about understanding how and why it works (or doesn’t) within the system it’s part of, and how it affects other areas of the system. Prompt Testers consider:
- How prompts interact with the model
- How that output affects user experience
- What variations may appear between models or updates
- Whether outputs are consistent, helpful, and safe
Testing prompts means testing systems, not just strings of text. And as LLMs become part of more products, traditional QA roles need to evolve. Testing an LLM-based feature isn’t about confirming that it once gave the right output, it’s about ensuring it consistently delivers the right kind of output, across contexts and over time. A test case that passes today might fail tomorrow without changes in your code—and that’s exactly why this kind of testing matters.
What Prompt Testing Actually Looks Like
Prompt testing is more methodical than people assume. It’s not just “does this sound good?, or having the prompt engineer run through the completions a couple of times. The right approach depends on the integration, the context, and the kind of output you’re aiming for.
Methods include:
- A/B testing: Running multiple versions of a prompt to see which performs better across different user goals.
- LLM-as-Judge: Using another LLM to evaluate responses for accuracy, precision, tone, or helpfulness.
- Algorithmic evaluation: Using pattern recognition, keyword scoring, or embeddings to measure semantic similarity.
- Verifiability: Setting up clear criteria for success and ways to measure it.
Different outputs require different approaches. For more creative or open-ended completions, we may lean on human evaluation or LLM/human alignment scoring. For structured or factual outputs, automated scoring might be enough.
In some cases, we might need to run load testing to evaluate large volumes of completions or monitor user feedback over time to detect degradation or edge cases.
The key is knowing what to measure and how to measure it. Measuring in a way that reflects the true value of the AI output in context.
Metrics Matter, So Does Domain Knowledge
While metrics are crucial, they don’t tell the whole story. You still need human expertise to judge nuance. That might mean collaborating with a medical expert, a customer service leader, or even a legal advisor, this will depend on what your AI is doing. Good prompt testing combines quantitative scoring with qualitative review. It’s both scientific and subjective.
Post-Launch: Don’t Set It and Forget It
Prompts aren’t one and done; models change, use cases evolve, users are interacting differently with AI everyday. What worked yesterday may degrade tomorrow. That’s why follow-up matters:
- Are prompts still producing consistent results?
- Have user expectations shifted?
- Is the model behaving differently after updates?
As systems evolve, Prompt Testers create versioning strategies, regression tests for LLMs, and workflow monitoring that alert teams when something goes off-track.
Why might your team need a Prompt Tester?
If your product involves AI, you likely already have a Prompt Engineer. If you’re putting that product into production, especially if it touches end users or critical decisions, you also need someone testing it.
You wouldn’t ship code without QA, the same applies to shipping AI prompts without Prompt QA!
Just like developers and QA form a natural pair, so do prompt engineers and prompt testers.
If you need prompt engineers or prompt testers AgilityFeat is here to help.
We’re helping clients assemble teams of Latin American talent not only to develop, but also to test AI-powered features that hold up in the real world. From prompt design to post-launch monitoring, we help teams scale LLM work without sacrificing quality or safety. Let’s talk!
