Generative AI in Software Testing: LLM Guide 2025

The global AI-enabled testing market reached $857 million in 2024 and is projected to grow at 20.9% annually through 2032, driven largely by generative AI capabilities (Source: Fortune Business Insights). But what exactly is generative AI in software testing and how does it affect your testing strategy?

​​Unlike traditional rule-based automation, generative AI powered by large language models (LLMs) can understand context, generate human-like text, create test scenarios autonomously, and even write and fix code. For CTOs, engineering managers, and QA leaders, understanding how generative AI works isn't just technical knowledge - it's becoming essential for keeping up with more AI-enabled competitors. 

This comprehensive guide explores how generative AI and LLMs transform quality assurance services, from understanding the underlying technology to practical implementation strategies that deliver measurable results.

Understanding Generative AI and Large Language Models (LLMs)

Generative AI, powered by LLMs, works by predicting the next word in a sentence. It sounds simple, but it allows AI to create clear and natural-sounding text. These models are trained on huge amounts of data - from books and research papers to online content — and learn the patterns between words and ideas.

The process breaks text into pieces (called tokens), turns them into numbers, and runs them through layers of neural networks to find meaning. With every training step, the model improves at giving contextually correct answers.

What Is Generative AI in Software Testing?

Generative AI in software testing refers to AI systems that can create new content - test cases, test data, bug reports, documentation - rather than simply executing predefined rules. At the core of this capability are large language models (LLMs), neural networks trained on vast amounts of text data to understand and generate human language.

How LLMs work in simple terms:

Generative AI, powered by LLMs, works by predicting the next word in a sentence. While this sounds simple, it enables AI to create clear and natural-sounding text that can describe test scenarios, generate documentation, or even write automation code.

These models are trained on massive datasets including books, research papers, code repositories, and online content. Through this training, they learn patterns between words, concepts, and ideas - enabling them to understand context and generate relevant responses.

The technical process:

1. Tokenization: Text is broken into pieces called tokens (words or word parts)
2. Numerical encoding: Tokens are converted into numbers the model can process
3. Neural network processing: Multiple layers analyze patterns and relationships
4. Context understanding: The model builds understanding of meaning and intent
5. Generation: Based on learned patterns, the model produces contextually appropriate output

With each training iteration, the model improves at generating contextually correct and useful answers - making it increasingly valuable for complex testing scenarios.

Current advantages of LLMs:

• Strong language understanding: They can understand and produce natural language very smoothly, allowing easier teamwork between people and machines.
• Versatile to apply: LLMs can be used in many areas - from testing to design and documentation. Their flexibility makes them useful for many testing situations.
• Fast once trained: Once trained, they give answers in real time, speeding up work and saving teams hours of repetitive QA tasks.
• Flexible: LLMs can handle huge amounts of data across projects. As organizations grow, AI scales with them without issues.
• Adaptable: They can be customised for specific industries or products, ensuring that testing fits each system’s needs.

Current weaknesses of LLMs:

• Need for good training data: Poor or biased data gives poor results. “Garbage in, garbage out” still applies.
• Need for clear context: Without clear information, AI can easily misunderstand. Context is key -  unclear prompts lead to unclear answers.
• Limited context size: Even advanced models can forget parts of long prompts. This limits how complex one task can be.
• Dropped information: LLMs can sometimes skip or lose parts of a conversation, leading to incomplete or inconsistent answers.
• Hallucinations: Around 20% of generated content can be inaccurate. Always check AI-generated data before using it in production.

Finally, it’s important to remember that LLMs don’t think or reason like people do. Essentially, they just predict patterns. Knowing this helps teams use them in the best way.

The strategic advantages of LLMs in software testing

For CEOs and CTOs evaluating investments in generative AI in software testing, understanding the specific advantages helps build the business case:

1. Strong natural language understanding

LLMs can understand and produce natural language with remarkable fluency, enabling easier collaboration between technical and non-technical team members.

Business impact: Product managers and business analysts can describe test requirements in plain English. LLMs translate these descriptions into technical test specifications, eliminating traditional communication bottlenecks.

Real-world application: “Test that users with more than 50 items in their cart can complete checkout" becomes a comprehensive test suite covering edge cases, error handling, and performance considerations - automatically.

2. Versatile application across testing activities

LLMs can be applied across the entire testing lifecycle - from test planning and case generation to execution analysis and documentation.

Testing areas enhanced by LLMs:

• Test case generation from requirements documents
• Test data creation with realistic variations
• API testing with intelligent request generation
• Documentation creation and maintenance
• Bug report generation with clear reproduction steps
• Test script maintenance and updates

Strategic advantage: When organisations use generative AI in software testing, they can reduce specialisation requirements. QA teams can leverage LLMs across multiple testing domains without hiring separate specialists for each area.

3. Scalability without linear cost growth

LLMs handle enormous amounts of data across unlimited projects. That’s why as your organisation grows, AI can scale automatically without proportional increases in testing resources.

Growth enablement: Traditional testing typically scales linearly - double your applications, double your QA team. Generative AI breaks this model, enabling 3-5x application growth with minimal testing team expansion.

4. Domain adaptability

LLMs can be customised and fine-tuned for specific industries, products, or technical stacks, ensuring QA testing aligns perfectly with your system's unique requirements. However, we should be particularly careful when testing in highly regulated and data-sensitive domains like finance, healthcare or aviation.

In another Dreamix article on AI in quality assurance my colleague Kiril Ivanov, Lead Quality Assurance professional discusses exactly that. QA teams can opt for using various AI tools to make our work faster and more efficient but we should remain cautious of potential dangers such as project data leakage as gen AI can be prone to that.  

Before implementing generative AI capabilities, organisations must assess whether their infrastructure, security protocols, and team skills are ready for AI adoption. Our comprehensive AI readiness guide helps CTOs and engineering leaders evaluate their organisation's preparedness across technical, cultural, and operational dimensions.

5. Real-time speed after training

Once trained or fine-tuned for your domain according to your IT and security department’s best practices, LLMs can provide instant responses, dramatically accelerating testing workflows.

Cost impact: This translates to hours saved per team member per week - time redirected toward exploratory testing, strategic planning, and innovation rather than repetitive documentation tasks.

Now, let’s look at the “language” generative AI engines operate with - prompts. 

The 8 principles of effective testing prompts

Prompt engineering's trajectory from promising standalone professional occupation in 2024 (with $100K+ salaries) to near extinction by mid-2025 reflects its rapid evolution from specialised role to foundational skill absorbed into broader technical positions. As confirmed by Fast Company and Indeed Hiring Lab data showing job postings declining from a 0.3% peak to nearly zero. 

But what’s actually behind the fancy term and how to apply it to software testing with AI successfully? Prompt engineering is the skill of communicating well with generative AI. It’s not just about giving commands, it’s about using language to work better together. Let’s explore how to make the most of your prompts when using generative AI in software testing:

1. Precision: Clarity drives quality

Be specific about what you want from gen AI. A specific question gives a specific, useful answer and vice versa - vague requests will only produce vague results. For example:

Vague prompt: "Create tests for the payment system"

Precise prompt: "Generate 15 test cases for a Stripe payment integration covering: successful credit card charges, declined cards, expired cards, insufficient funds, network timeouts during processing, webhook handling for async notifications, refund scenarios, and PCI compliance validation. Format as Gherkin Given-When-Then scenarios."

2. Tone and style: Consistency in output

Set the target voice and technical level to ensure outputs match your team's standards. For example, set up a project and ask the gen AI to write as an “experienced test engineer” or “technical writer” to keep the output consistent.

Prompt addition example: "Write in the style of an experienced senior QA engineer at a Fortune 500 company. Use professional but accessible language. Avoid excessive jargon while maintaining technical accuracy."

Consistent style reduces cognitive load when reviewing AI-generated content. Your team can focus on technical accuracy rather than rewriting for voice and tone.

Read next: In-House vs Outsourcing Software Development: The Fortune 500 Approach

3. Persona: Role-based context

Assign the AI a specific role or expertise area to focus its responses appropriately.  The right persona helps keep responses focused.

Effective persona examples for testing can sound like: "You are a cybersecurity-focused QA engineer with 10 years of experience in financial services"

4. Step-by-step instructions

Give step-by-step directions. This helps the AI produce clear, actionable results. Structured instructions reduce ambiguity and increase output quality.

This can be an example sequence of steps: 

Step 1: Analyze the following API documentation [paste docs]

Step 2: Identify all endpoints that handle payment processing

Step 3: For each endpoint, list potential failure scenarios

Step 4: Generate test cases covering happy path, edge cases, and error conditions

Step 5: Format as JSON with fields: testID, endpoint, method, scenario, expectedResult, priority

5. Scope definition: Boundaries prevent overload

Set limits on the response. Keeping scope clear saves time and avoids long or messy outputs.
Scope-setting examples: "Generate no more than 10 test cases, focusing only on critical path scenarios" or "Focus exclusively on authentication and authorisation; we'll handle data validation separately"

Time savings: Clear scope reduces irrelevant content by 70-80%, cutting review time significantly.

6. Constraints: Rules for relevance

Add specific rules or filters to remove noise from outputs. Some useful constraints you can apply when while using generative AI in software testing:

• "Exclude any test cases requiring third-party services we don't have access to"
• "Do not generate tests requiring production data; all tests must use synthetic data"

7. Examples: Show, don't just tell

Show what a good answer looks like. Examples help the AI match your expectations.

Generate test cases for user registration. Here's the format I want:

8. Feedback loops: Iterative refinement

Improve prompts based on AI outputs. Treat prompt engineering as an iterative process, not a one-time effort.

Feedback prompt pattern: "The test cases you generated missed these edge cases: [list gaps]. Please regenerate these scenarios, and suggest 5 additional edge cases I might have overlooked."

Learning benefit: This two-way dialogue improves both your prompts and the AI's understanding of your requirements.

Prompt engineering is becoming an essential skill - a new kind of digital literacy - for anyone working with AI.

Practical prompt engineering techniques for testing

Zero-shot prompting

Ask a question with no extra context. Best for simple or factual queries and generating basic test data.

Example: "What are the key test scenarios for a password reset feature?"

Limitations: May produce generic results lacking your organization's specific requirements.

Few-shot prompting

Provide 2-5 examples to help the AI understand the pattern, style, or format you want.

When to use:

• Generating tests matching your team's existing format
• Creating tests for features similar to ones you've tested before
• Maintaining consistency with established test documentation

3. Chain-of-Thought Prompting

Ask the AI to break up complex testing scenarios step-by-step. Best for intricate logic, multi-system testing, or when you need to verify the AI's reasoning.

When to use:

• Complex integration testing scenarios
• Performance testing strategy development
• Security testing attack vector identification
• Root cause analysis of test failures

Reusable prompt patterns for testing teams

As prompting transitions from specialised role to expected baseline skill, these templates ensure your team stays ahead of the curve. And remember that good prompt design is key to effective AI collaboration.

• Input Semantics: Show how inputs should produce specific outputs. This keeps communication consistent.
• Output Semantics: Define output formats like JSON, TypeScript, or Markdown. Consistent formats help integration.
• Reflection Pattern: Ask the AI to check and correct its own mistakes. This improves accuracy easily.
• Prompt Improvement: Ask the AI how to improve your prompt. This helps both you and the model learn.
• Interaction: Have the AI ask clarifying questions. This creates a two-way problem-solving session.
• Context Control: Limit its knowledge to a given dataset. This keeps focus and protects sensitive data.

Ethical considerations and bias in prompts 

As AI becomes more and more embedded in testing workflows, maintaining ethical standards ensures that automation serves human judgment rather than replacing the critical thinking that keeps software safe, fair, and trustworthy.

• Fairness: Avoid promoting stereotypes or biased data. Testing should treat all users equally.
• Collaboration: Use AI to support people, not replace them. Human creativity and diversity of thought still matter most. This human-centered approach extends beyond prompt engineering to the entire quality assurance process. For a deeper exploration of how to integrate AI across your QA lifecycle while maintaining quality standards, see our article on AI in quality assurance best practices.
• Transparency: Track where and how AI is used in testing. This builds trust with clients and stakeholders.

Keep in mind that generative AI in software testing is still a support tool and AI alone can’t and should’d make any judgment calls. Ethical awareness ensures it’s used responsibly. 

When (and why) not to use AI

AI offers many benefits - but it’s also important to know its limits. Critical thinking keeps AI a helpful partner, not a risky one.

• Bias: Training data may miss important edge cases, creating blind spots in testing. This risk is particularly acute in safety-critical systems where incomplete test coverage can have catastrophic consequences. In specialised domains like aviation - where we've implemented FAT and SAT testing for air traffic management systems - the stakes demand human expertise that AI cannot yet replicate.
• Privacy and Security: Sensitive data can leak or be misused. Always control what data you share with AI tools.
• Hallucination: AI can generate false but convincing content. Always verify before using results.
• User Behavior: Over-reliance can lead to carelessness. Make sure to always make the final judgement.

The human factor: Collaborating with AI

No matter how advanced AI becomes, it cannot replace human intelligence. Humans bring context, empathy, and moral judgment that machines cannot copy. That’s why it’s crucial to always keep a Human-in-the-loop

Interpretation: Humans understand subtle meaning and ambiguity. Machines still struggle with complex intent.

Creativity: People can imagine beyond the data. Innovation often begins where patterns stop.

Ethical Judgment: We can think about impact and responsibility. Accountability must remain human.

The future lies in cooperation, not competition. The goal is not to replace human testers with AI, but to empower them through it. When humans and machines collaborate - wisely, ethically, and creatively - the result is smarter, faster, and more comprehensive software testing that drives better business outcomes.

Implementing this collaborative model often requires partnering with organizations that have proven expertise in both AI technologies and software testing. When evaluating potential partners, understanding what distinguishes top AI software development companies helps ensure you're working with vendors who can deliver both technical excellence and strategic guidance

At Dreamix, our 19 years of software development experience combined with cutting-edge AI capabilities position us to guide organisations through this transformation. We've helped enterprises across healthcare, aviation, transportation, and manufacturing implement generative AI testing strategies that deliver measurable results while maintaining the quality standards their businesses demand.