Stop AI Hallucinations in Software Testing: Why Context Engineering is Your New MVP

The rapid growth of Artificial Intelligence (AI) promises to revolutionize software testing, offering unprecedented gains in both speed and accuracy. However, to truly harness this power, the testing community must confront a fundamental AI challenge: AI hallucinations in software testing. When AI systems hallucinate, the results they produce are unreliable, rendering automation ineffective. The solution lies in providing context engineering for QA, which directs the AI, eliminates unreliable outputs, and transforms your testing results.

This blog explores why context is vital for AI, how AI-powered QA outcomes improve with context, and the techniques you can use to engineer the perfect context for your QA cycles.

Context: A Familiar Concept in a Digital World

We interact with context-driven systems daily, confirming that personalised, relevant information is based on knowing the user history or general behaviour.

Consider these common examples:

• Netflix: When Netflix suggests “we think this is what you will love,” it is predicting your preferences based on your past viewing history. This “knowledge about your past” is the context that ensures what you see is tailored to you, and different from what another user sees.
  
• Amazon Shopping: When searching for a laptop, Amazon utilizes context built over time from millions of laptops sold. This data allows the platform to predict related items people buy (like stands, bags, or refurbished units). This relies on general user behaviour, the context, to redirect new shoppers in a direction most users follow.
  

Whether personalized (like Netflix) or based on general user behaviour (like Amazon), these systems demonstrate the power of context in AI systems in delivering relevant outcomes.

Why AI is Stateless: The Memory Problem

The default understanding of AI often suggests it can “think or work like a human”. In reality, AI does not yet fully behave like a user, primarily because it lacks session memory in AI testing.

In computer language, AI models are typically stateless. Interactions are highly transactional: you ask a question, the AI provides a response, and then that interaction is largely forgotten. The AI does not automatically carry information from one conversation to the next.

This stateless design is intentional and driven by two major considerations:

1. Scale and Performance: Highly performant systems designed to handle massive scale, like the millions of hits daily for major platforms, are generally built using stateless systems. This design choice allows large companies to scale effectively.
   
2. Security and Privacy: If AI were to remember everything, it would pose significant security and privacy risks, making users uncomfortable. By default, models are stateless to build trust, ensuring the user has control over what the AI remembers and when it forgets.
   

Therefore, if you want the AI to augment user behaviour and perform tasks specific to your application, you must explicitly provide context engineering for software testing. This context allows you to drive the specific behaviour you require for your application and use cases.

How Context Transforms Testing: Speed and Accuracy

Providing context for AI in QA directly addresses the AI stateless nature, significantly improving quality assurance outcomes.

1. Increased Reliability and Tailored Output

Context ensures reliable output by directing the AI to the specific area and depth you are interested in. For example, if you mention the word “bank,” context (based on the surrounding discussion) should ensure the AI understands you are referring to a financial bank rather than a river bank. Furthermore, context allows you to tailor the output to your needs, controlling how much direction you give to balance accuracy with speed. If you provide excessive context, the result may be perfect but slower; thus, you choose the level of context that ensures you are happy with both the speed and accuracy.

2. Fewer False Positives

Since AI is known to hallucinate, providing context reduces false positives in AI test automation by narrowing scope and focusing only on relevant data. This improves reliability for AI-powered QA testing.

3. Accelerated Testing Cycles

When accuracy improves, speed inherently increases because less time is spent fixing, redoing, or maintaining work. Better accuracy leads to faster, more efficient execution, thereby accelerating overall AI QA testing cycles.

Context Engineering: Five Sophisticated Methods

Providing context, often referred to as context engineering for QA, can be achieved through various methods that increase in sophistication.

1. Prompts: Use prompt engineering for test automation to guide the AI with clear instructions.
   
2. Expert Mode: Instruct the AI to assume a role such as “expert QA engineer” or “expert manual tester” to ensure domain relevant output.
   
3. Few Shot Learning: Provide examples of desired input and output. This teaches the AI exactly what to do for similar use cases.
   
4. Retrieval Augmented Generation (RAG) for Testing: RAG is a highly efficient way to provide context. It extracts only relevant context (for example, the payments module) instead of the entire repository. This prevents AI hallucinations in test automation.
   
5. Session Memory: Carry forward prior conversation history, allowing the AI to refer back to previous statements. This is critical for AI-powered QA accuracy.
   

The Optimal Context for QA

When focusing on QA, the type of information you feed the AI builds an essential picture of the application and its business intent. Contextual inputs include:

• Audio Video Content: Recordings of training videos, business meetings, application walkthroughs, or training content can be used to generate documents or AI test cases.
  
• Figma Designs: Walking through Figma designs provides visual context, which the AI can use to generate test automation scripts.
  
• Documents and Diagrams: JIRA stories, Confluence pages, requirement documentation, and architecture diagrams provide context about the system understanding and requirements.
  
• Code Comments: Comments written when code is checked in provide context about the intent behind changes.
  
• Code: Ingesting the code helps the AI understand technical aspects. Code alone is insufficient because it does not convey the business intent.
  

The optimal context combines business intent and code, ensuring generated test cases are robust and accurate.

Context in Practice: Generic vs Detailed Testing

The difference context makes is clear when generating test cases for a funds transfer use case.

Generic AI: Produces a high level list of test cases but misses specifics.

Context Aware Testing: A context driven QA automation platform can generate:

• A full list of prerequisites
  
• Detailed, step by step instructions
  
• Automation ready outputs
  

This granular output stops AI hallucinations in QA testing, reduces errors, and accelerates overall testing cycles.By leveraging context engineering for QA, teams can move from high level suggestions to precise, automation-ready AI test cases, effectively taking the grunt work out of QA.