Prompt Engineering in Testing – Quality Assurance

The world of Quality Assurance (QA) is undergoing a dynamic transformation, fueled by the rapid development of artificial intelligence (AI) and, in particular, large language models (LLM). In this new landscape, a fascinating and increasingly desirable specialty is emerging: Prompt Engineering in Testing. This is not just a fad, but a potentially fundamental change in the way we ensure software quality. At ARDURA Consulting, we are following these trends closely, helping companies understand and leverage the potential of AI in QA processes.

What is Prompt Engineering in software testing?

Prompt Engineering, in the context of QA, is the art and science of precisely formulating queries, or prompts, directed to AI models (such as GPT, Claude or Gemini). The goal is to produce valuable results to support the testing process. This is not about loose conversation with AI, but about strategically instructing the model to effectively support QA activities. This can include generating test cases – both positive, negative and edge – creating test data with specific parameters, helping write automation scripts, analyzing test results for anomalies, or identifying potential risk areas. A specialist in this field, the QA Prompt Engineer acts like a translator and guide, converting test needs into language that AI can understand, and then critically evaluating the results obtained.

How have developments in artificial intelligence affected software testing?

The development of AI, and especially the easy availability of powerful LLMs, is having a huge impact on traditional approaches to QA. We are seeing a clear shift from fully manual processes, or those based on rigid automation scripts, to hybrid models where AI becomes the tester’s intelligent assistant. AI offers the potential to significantly speed up many tasks, such as generating initial test sets or analyzing large data sets. It can also help increase test coverage by suggesting scenarios that might be missed by a human. At the same time, however, the introduction of AI raises new challenges. These concern the quality and reliability of the artifacts it generates, the need for QA teams to learn new skills, and the inevitable ethical issues. AI is not a replacement for critical thinking, but it is becoming a tool that, skillfully used, can significantly improve the effectiveness and reach of QA activities.

What skills are key for a Prompt Engineering specialist in QA?

The Prompt Engineer role in QA requires a unique synergy of technical, analytical and communication skills. A solid foundation in software testing is crucial, including a thorough understanding of QA principles, test design techniques, risk analysis and the software development lifecycle. Also essential is an understanding of how AI/LLM models work, their capabilities, but most importantly their limitations, such as their tendency to hallucinate or reproduce biases. Equally important are well-developed communication and language skills, allowing precise formulation of prompts. Analytical and critical thinking, enabling the dissection of problems and, most importantly, skeptical evaluation of AI-generated answers, prove indispensable. Knowledge of the business domain of the application under test provides the context necessary to create relevant queries. While not always required, programming basics can make the job significantly easier, especially with automation prompts. The whole is complemented by curiosity and a willingness to experiment, as prompt engineering is often an iterative process.

How is Prompt Engineering revolutionizing traditional testing methods?

Prompt Engineering does not so much replace traditional testing methods as it enriches and transforms them , acting as a gas pedal and source of new ideas. Instead of starting from a blank page, a tester can use AI to instantly generate initial sets of test cases based on requirements, which he then verifies and refines. It becomes much easier to create diverse test data with specific parameters. What’s more, LLMs trained on huge datasets can sometimes suggest unusual edge scenarios, broadening the tester’s perspective. AI can also assist in writing automation scripts, generating code skeletons that a QA engineer then adapts. Finally, it comes in handy for analyzing and summarizing results, efficiently processing long logs or reports. Thus, a paradigm shift is taking place – from performing all tasks on its own, to strategically using AI as a partner, while maintaining full human control over the quality of the process.

What challenges do testers face using AI models?

Despite its promising potential, the use of AI in testing presents significant challenges. The most important is the problem of unreliability and the tendency of LLMs to hallucinate – generating answers that look correct but are wrong or made up. This forces testers to constantly perform meticulous verification. Another challenge is the indeterminism of models, where the same prompt can produce different results, making repeatability difficult. The difficulty in validating AI responses is significant – assessing the value of a generated test case still requires human expertise. Also, the risk of replicating biases (bias) present in AI training data cannot be ignored. Data security and privacy issues when using external models are extremely important. The cost of API access and training investment must also be considered. Finally, in a rapidly changing environment, maintaining and updating prompts becomes a new maintenance task. Awareness of these challenges is crucial for effective and responsible use of AI in QA.

Fiche 1: AI Challenges in Testing

Don’t trust unreservedly: AI can “hallucinate.” Always verify!
Beware of bias: Models can replicate biases. Look for them actively.
Protect your data: Don’t send sensitive information to external AIs without safeguards.
Count the costs: API usage and training are real expenses.
Be flexible and critical: AI results are sometimes unpredictable and need to be evaluated.

Can Prompt Engineering automate end-to-end testing processes?

Currently, full automation of complex end-to-end (E2E) tests using Prompt Engineering alone is unrealistic and high risk. E2E tests simulate complex user paths and require precise interactions with multiple system components. Language models themselves cannot directly perform these interactions – clicking buttons or filling out forms.

However, Prompt Engineering can significantly support and accelerate the E2E automation process. AI can effectively generate E2E script frameworks in popular frameworks (Selenium, Cypress, Playwright), which QA engineers then complete and adapt. It is also an excellent tool for creating the test data needed to execute E2E scenarios. It can also help identify potential E2E flows based on requirements analysis and support analysis of test execution results to help diagnose the causes of failures. Prompt Engineering thus becomes a valuable support in the E2E test lifecycle, but does not replace dedicated test execution tools or human oversight of test logic and coverage.

How to effectively construct prompts for software testing?

The effectiveness of using AI in QA depends largely on the ability to create effective prompts. It’s an iterative process, but one based on several key principles. First and foremost, the prompt must be precise and clear, avoiding ambiguity. You should provide the model with adequate context, explaining the purpose of the test, the specifics of the application or the role the AI is expected to take (e.g., “Act like an experienced tester…”). It is also worth specifying the expected output format (list, table, code, Gherkin scenario). A very effective technique is to give 1-2 examples of the expected output (so-called few-shot prompting). For complex tasks, it is better to divide them into smaller steps and generate answers in stages, rather than creating a single, very complex prompt. Keep in mind that rarely is the first prompt perfect – the key is to analyze the response and iteratively refine the query. This is an ongoing dialogue with the model, requiring patience and experimentation.

What are the business benefits of implementing Prompt Engineering in QA?

The implementation of Prompt Engineering in QA processes, while requiring an initial outlay, can translate into tangible benefits for the business. The most obvious is speeding up test cycles, resulting from automating the generation of cases, data or script fragments. AI can also help increase test coverage by identifying scenarios that could be missed, leading to higher product quality. While new costs need to be considered, there is potential to reduce costs associated with manual repetitive tasks. More efficient testing means faster error detection, which reduces the cost of fixing errors. Importantly, Prompt Engineering helps increase the efficiency of the QA team by relieving testers of routine tasks and allowing them to focus on more complex, strategic tasks. Ultimately, all of these factors contribute to delivering more stable and reliable products to the market, which builds customer confidence.

How to start a career in Prompt Engineering in testing?

Entering the world of Prompt Engineering in QA is a promising growth path for testers interested in new technologies. The foundation remains a solid QA foundation – without an understanding of testing principles, it is difficult to use AI effectively. The next step is to learn about the world of AI and LLM, understanding how they work and their limitations, preferably through hands-on experimentation with available tools. It is crucial to practice the art of writing prompts, starting with simple tasks and gradually increasing complexity. It is important to combine the tester’s knowledge with AI capabilities, identifying specific tasks in daily work that can be improved. Since the field is evolving rapidly, continuous learning through articles, blogs or webinars is essential. It’s also worth documenting your experiments by creating a personal portfolio. Keep in mind that this is more of an evolution of the tester’s role, requiring a combination of QA competencies and the ability to use AI effectively.

What certifications and training are worth considering on the Prompt Engineer career path in QA?

The market for dedicated certifications for Prompt Engineering in QA is just developing. At present, it makes sense to focus on building competence in several areas. Certifications that cover the basics of testing, such as ISTQB Foundation Level and higher, are invariably important. Introductory courses in AI and Machine Learning, available on learning platforms (Coursera, edX) or offered by cloud providers (AWS, Azure, GCP), will also help. Specific training courses in Prompt Engineering that provide practical knowledge are also emerging, although their formal recognition may still be limited. Familiarity with the documentation and capabilities of popular AI models is also key. However, practice and experience remain the most important – experimentation with tools and critical analysis of results are more valuable than any certification. It is also worth actively following industry publications and attending conferences.

Which industries are the fastest adopters of Prompt Engineering in testing processes?

Adoption of Prompt Engineering in QA has been uneven. The fastest to implement the new methods are sectors that have already invested heavily in automation and where the pressure to bring products to market quickly is greatest. These primarily include the technology sector (Software/SaaS), where companies are naturally experimenting with optimizing their own processes. Booming e-commerce and retail are also eager for tools that accelerate testing. Similarly, FinTech and financial services, despite regulation, often invest in innovative QA technologies. Fast production cycles in the media and entertainment industries also favor AI adoption. In contrast, heavily regulated sectors such as pharmaceuticals, aerospace and automotive (especially critical systems) are taking a much more cautious approach, using AI mainly as a support for ancillary tasks due to the need for rigorous validation and determinism.

How to measure the effectiveness of Prompt Engineering-based testing?

Measuring the effectiveness of AI-assisted testing requires a new approach. It is not enough to count generated test cases; measuring added value is key. It is necessary to assess the quality of the generated artifacts – their correctness, completeness and relevancy. It is important to check whether the use of AI has helped increase test coverage, especially in the area of edge scenarios. An important metric is the time saved on the task compared to traditional methods. The defect detection rate should also be analyzed – does AI-inspired testing effectively find bugs? It is also necessary to cost vs. benefit analysis , taking into account the expense of AI tools and validation time. It is also worth taking into account the QA team’s subjective assessment of whether AI actually makes the job easier. Remember not to treat metrics in isolation and focus on the real impact on product quality and process efficiency.

Fiche 2: Measuring AI Success in QA.

Not quantity, but quality: It’ s the value that counts, not the number of tests generated.
Validation is key: Evaluate the correctness and relevancy of AI results.
Measuring time and coverage: Does AI help you test faster and wider?
Look for defects: Are AI-inspired tests effective at finding defects?
Analyze costs: Is the investment in AI paying off?

What are the limitations of using language models in testing?

Large language models (LLMs) have important limitations that must be taken into account. The most important is the tendency to hallucinate and generate false information. Models do not have a “true” understanding of context or business logic, operating on linguistic patterns. They have problems with complex logical reasoning and mathematics. They are sensitive to minor changes in prompt formulation. They may replicate biases (bias) from training data. Their “context window” (the amount of information processed at once) is limited. Importantly, LLM cannot directly interact with the system under test – it requires additional tools to do so. These limitations mean that AI is a powerful assistant, but it cannot replace human knowledge, critical thinking and real-world test execution.

How does Prompt Engineering complement traditional tester skills?

Prompt Engineering does not replace, but valuefully complements and extends the tester’s competence. The introduction of AI shifts the focus of his work. There is a shift from creating everything from scratch to verifying and refining AI proposals. The tester has more time for strategic tasks such as risk analysis, exploratory testing or designing complex scenarios. Prompt Engineering becomes a new tool in the tester’s arsenal, alongside automation and manual testing. Even more important is the role of the critical thinker, able to evaluate AI results and decide how to use them. It also requires continuous learning and adaptation. Prompt Engineering empowers the tester, allowing him to operate more efficiently and at a higher level of abstraction.

What will the future of the software testing profession look like in the AI era?

The AI era is bringing an evolution, not an elimination, of the testing profession. The role is becoming more strategic and analytical. The future tester is likely to be a quality curator aided by AI, using it to generate ideas and artifacts, but key in validating them. The ability to evaluate AI systems (AI validation) will become increasingly important. The human role in exploratory testing and complex scenarios that require creativity and intuition will remain indispensable. The tester will be the architect of test strategy, combining different approaches (manual, automation, AI). The role of guardian of ethics and impartiality will also gain importance, ensuring the elimination of bias in testing processes. The profession is becoming more intellectually demanding, and the key to success will be continuous learning, adaptability and treating AI as a powerful tool.

What practical Prompt Engineering use cases work well for testing?

Prompt Engineering finds application in many daily tasks of a tester. Some of the most effective include generating a variety of test data (positive, negative, edge), creating initial sets of test cases based on requirements or user stories, generating scenarios in BDD/Gherkin format, creating automation script frameworks (UI, API), suggesting ideas for exploratory testing, summarizing test results and analyzing logs to identify error patterns, helping translate requirements into acceptance criteria, and identifying potential ambiguities in specifications. The key is to start with simpler tasks and always critically review the results.

How do you integrate AI-based testing with existing DevOps methodologies?

Integrating AI-assisted testing into CI/CD pipelines is possible and can be beneficial, but requires caution. AI model APIs can be used to automatically generate tests “on the fly” (e.g., for new code) or to analyze results (e.g., summarizing errors from logs). However, it is crucial to introduce quality gates with validation steps – AI results cannot pass automatically. AI can also support testing infrastructure as code. Monitoring and a feedback loop is also important, where production results can influence test improvements. Integration requires good DevOps practices, versioning prompts and most importantly – control mechanisms to avoid automatically introducing AI-generated errors.

Will Prompt Engineering replace traditional testing tools?

Definitely not. Prompt Engineering and traditional testing tools play complementary roles. Tools such as Selenium, Cypress, Postman and Jira focus on performing interactions with the application, managing the test process and storing the results. Prompt Engineering, using LLM, focuses on supporting the creation of test artifacts (cases, data, scripts) and analyzing and interpreting the information. LLM acts as an intelligent assistant supporting the tester before and after test execution, while traditional tools are necessary for the execution itself. The future lies in their synergistic use.

How to prepare the QA team to implement Prompt Engineering-based methods?

Preparing the QA team is a key element for success. This requires education and training on AI, LLM and prompting techniques. Access to AI tools in a safe environment for experimentation must be provided. It is important to develop internal guidelines for prompt creation, validation, safety and ethics. It is worth starting with small experiments so that the team can build trust and understanding. It is crucial to promote a culture of critical thinking – AI is an assistant, not an oracle. A space should be created to share knowledge and experience within the team. The entire process should be evolutionary, supported by management and focused on real benefits to the team and product quality.

What are the most common mistakes when implementing Prompt Engineering in testing?

During implementation, it is easy to stumble. The most common mistakes include overconfidence in AI and failure to critically validate results, formulating prompts that are too general or vague, ignoring LLM limitations (e.g., logic problems), failing to provide domain context in queries, treating AI like a “black box” without trying to understand its operation, insufficient validation of generated artifacts, neglecting data security and privacy issues, and failing to take an iterative approach to improving prompts. Avoiding these mistakes requires awareness, discipline and regularity.

How to optimize prompts for more effective testing?

Optimizing prompts is an ongoing process to get the best possible responses from AI. Effective techniques include adding details and specifying constraints, defining roles/persona for the AI, specifying negative constraints (what the AI is not supposed to do), using examples (few-shot prompting), encouraging the model to think step-by-step (chain-of-thought) on complex tasks, experimenting with model parameters (e.g., temperature) and, most importantly, iteratively refining the prompt based on analysis of previous responses. Systematic application of these techniques allows us to “teach” the model our expectations.

What ethical considerations should be made when testing with AI?

The use of AI in testing raises important ethical questions. The risk of reproducing biases (biases) present in AI training data that could lead to inadequate testing for certain user groups must be actively addressed. Data privacy is key, especially when using external models – anonymization or synthetic data should be used for sensitive information. Transparency of processes becomes important – whether and how to communicate the use of AI in quality assurance. Accountability should also be clearly defined for errors or problems resulting from the use of AI. Testers become the guardians not only of technical quality, but also of the ethical dimensions of the testing process.

How does Prompt Engineering in testing affect time-to-market and cost-to-market?

The implementation of Prompt Engineering in QA has the potential to positively impact the time (Time-to-Market) and cost of product development, although this is not guaranteed and depends on the maturity of the implementation. Speeding up test cycles by automating the generation of artifacts can reduce the overall time required for testing. Faster error detection reduces the cost of fixing errors at later stages. Increasing the efficiency of the QA team can allow the required quality to be achieved faster with the same resources. However, it is important to keep in mind upfront costs (tools, training) and ongoing costs (API usage, validation time). In an optimal scenario, strategic use of AI allows faster delivery of a better tested product, which translates into competitive advantage and potential savings, but this requires conscious management and measurement of efficiency.