Adding generative AI to an application can create impressive capabilities.

But testing an AI feature is fundamentally different from testing a traditional form, API or calculation. Traditional software often behaves deterministically:

  1. Input A
  2. Output B

Large language models can produce different responses to similar inputs. That changes how quality must be evaluated.

Start with the intended purpose

Before testing an LLM feature, define what it is supposed to accomplish. For example:

  • Summarize documents
  • Answer customer questions
  • Generate reports
  • Extract information
  • Create marketing content
  • Assist employees

Without clearly defined expectations, teams can easily confuse an impressive demonstration with a reliable product.

Test accuracy

Where answers depend on factual information, determine whether the system produces responses grounded in appropriate information. Test:

  • Common questions
  • Complex questions
  • Ambiguous requests
  • Incomplete information
  • Incorrect assumptions

Test hallucination risk

LLMs may generate information that sounds convincing even when it isn't supported.

Testing should determine how the application behaves when it doesn't know the answer.

In many business applications, acknowledging uncertainty is safer than confidently generating unsupported information.

Test prompt injection and abuse cases

AI applications also introduce security concerns.

Teams should test how the system responds when users attempt to manipulate instructions, retrieve restricted information or bypass intended boundaries.

Test sensitive information handling

Determine what information enters the model and where it goes. Ask:

  • What data is submitted?
  • Is personally identifiable or confidential information involved?
  • Is information logged?
  • How long is it retained?
  • Who can access it?

Test consistency

Run important scenarios repeatedly. One successful response isn't enough evidence of reliable behavior.

Measure what matters

Create evaluation criteria relevant to the application. These might include:

  • Accuracy
  • Relevance
  • Completeness
  • Groundedness
  • Safety
  • Response time
  • Cost
  • Consistency

AI still needs quality engineering

AI changes software testing. It doesn't eliminate it.

Organizations introducing AI need an evaluation strategy that combines traditional software testing with AI-specific quality, security, privacy and reliability considerations.

At Qanovix, we help organizations evaluate AI-enabled applications before those capabilities reach customers.