Prompt-to-Test: Our take on why it doesn’t work. 🤖

Let’s talk about the hype around “prompt-to-test” for software QA. It sounded like a dream—a quick text prompt leading to automated, seamless end-to-end testing. In theory, it promised to revolutionize how QA teams work.
However, the reality has been underwhelming.

The Problem with Prompt-to-Test

Prompt-to-test promised to streamline QA by cutting down back-and-forth. But the reality was more complex. PMs were drawn to the idea, thinking they could handle testing directly with minimal onboarding. Meanwhile, Devs and QAs knew that testing goes beyond context—it requires technical expertise and an understanding of complex workflows, which these systems couldn’t provide. As a result, they were left with incomplete coverage and missed edge cases.

The Reality

As teams began implementing prompt-to-test systems, the limitations quickly became clear. While the concept worked in isolated scenarios, it struggled to deliver when faced with the complexities of real-world QA.

Context is Non-Negotiable
QA isn’t just about checking boxes—it’s about understanding the product. Each feature and user flow needs context to be properly tested. Prompt-based systems skim over this depth, relying on minimal input and forcing teams to constantly reintroduce context. This results in wasted time and shallow tests that miss critical coverage.
Inconsistent Results in Real-World Scenarios
QA evolves with the product, but prompt-based tools can’t keep up. These systems were expected to be as intuitive as ChatGPT, but in practice, they’ve been inconsistent, particularly in complex apps. Generated tests often need heavy human intervention, which negates the efficiency they promise—especially for large, interconnected systems.
Clear Industry Pivot
The flaws of prompt-to-test are visible in the industry. Companies like Kane AI, Mobile.dev, and Camel QA have pivoted from this approach, realizing it can't handle real-world QA complexity. The shift away from prompt-driven automation is a clear sign it’s not living up to the hype.

So, What Now?

At Quash, we’ve taken a different route. Rather than focusing on just executing tests, we prioritize the most critical aspect of QA: understanding the product. We believe effective testing starts with a deep understanding of how a feature fits into the broader product and how it interacts with other systems.

Instead of focusing solely on execution, we prioritize the entire lifecycle of testing. This means deeply understanding the product, mapping the impacts of new code, and defining precise, comprehensive test cases and executing them. By integrating context into every step, we deliver actionable insights that elevate testing from a checklist task to a strategic advantage. It’s about addressing the complexity of QA head-on, not sidestepping it.

We’re not here to follow the hype. We’re here to solve problems. Prompt-to-test isn’t the future of QA because it doesn’t address the realities of building and testing modern software. Instead, we’re focused on building solutions that embrace context, deliver consistency, and reduce time-to-market—without cutting corners.

Prompt-to-test didn’t work—and that’s okay. We’re building something that will.

Cursor - The AI Code Editor You Should Know About

You know that feeling when you come across a tool so good you have to share it? That’s exactly what happened to Hemanth on our team when he came across Cursor.

Unlike tools like GitHub Copilot, which act as extensions to existing IDEs, Cursor stands out by being a fully integrated development environment (IDE) on its own. It’s not just helping you write code—it’s reshaping how you think about coding. The key differentiator - Context. It understands your codebase, offering smarter suggestions and guidance.

Its built-in chat interface takes things further, allowing you to discuss and apply changes effortlessly. Powered by the Claude model, it has advanced features like autocompletion, optimization, and delivering near-accurate results when paired with clear prompts.

Our team has been experimenting with Cursor, and the results have been promising. If you’re curious, we’d definitely recommend giving it a try. You might even find it as transformative as we have.

Prompt-to-Test: Our take on why it doesn’t work. 🤖

The Problem with Prompt-to-Test

The Reality

So, What Now?

​Cursor - The AI Code Editor You Should Know About

Cursor - The AI Code Editor You Should Know About

New Year, New QA Insight: Why Prompt-to-Test Isn’t the Future + A Must-Try Tool