When I started as Quality Assurance on our recent GenAI project turning unstructured SOWs into structured insights, I thought I knew what testing meant. Eight weeks later, having validated 42 entity types across 100+ documents, I realized that QA in the GenAI space isn’t just different, it’s a completely new discipline.

I have tried to sum up the four biggest things I learned during the process.

1. The Reality Check: Accuracy Isn’t Binary

Traditional software testing gives you clear pass/fail criteria. A button either works or it doesn’t. A calculation is either correct or incorrect. But in document-based AI? Accuracy becomes contextual, nuanced, and surprisingly subjective.

Consider this: Our LLM extracts “Project Manager: Sarah ” from a 50-page SOW. Seems straightforward, right? But what if:

1. Sarah Johnson is mentioned as the interim project manager in section 2
2. The actual project manager (John) is only referenced in an appendix
3. A table on page 23 shows Sarah transitioning to a different role mid-project

Which extraction is “correct”? The answer depends on business context, document structure, and stakeholder priorities. This is where QA in GenAI shifts from bug-hunting to truth-definition, where accuracy depends on context, not code

2. From Gatekeeper to Translator

In our project, we were extracting 42 different entity types from budget allocations to SLA definitions, across documents with wildly different formats. Some SOWs buried critical information in cross-references. Others embedded key data in complex tables or scattered it across multiple appendices.

I quickly learned that my role wasn’t just to catch errors, it was to become a translator between model behavior and business rules. When the model extracted a delivery date as “Q2 2024” but the business needed a specific calendar date, was that wrong? Or was our validation criteria incomplete?

This shift in perspective changed everything. I wasn’t just testing outputs, I was actively shaping the definition of “correct” across documents with varied structures.

3. Building Validation Logic That Scales

Working with 42 entities across 100+ documents, we built a detailed Excel-based accuracy matrix, logging values per entity, per document. This wasn’t just line by line checking, it became the analytical backbone of our QA approach.

Here’s what made our validation approach effective:

Structured Logging Beyond Pass/Fail
We moved past simple correct/incorrect checks by logging source context, confidence, business impact, and recurring error patterns, creating richer QA insights.

Domain-Aware Validation
Accuracy wasn’t just technical, it had to make business sense. We built validation rules for hierarchy, cross-document consistency, and logical business rules.

Iterative Truth Definition
“Ground truth” wasn’t fixed, it evolved. QA often meant defining rules in ambiguous cases, shaping how correctness itself was measured

4. The Investigation Mindset

Traditional QA asks: “Does this work as expected?”

GenAI QA asks: “What did the model actually understand, and how do we bridge the gap between that understanding and business value?”

This turned me into an investigator. When the model consistently missed budget information in certain document types, I didn’t just log it as a failure. I traced the logic:

• How was the model interpreting section headers?
• Were certain formatting patterns confusing the extraction?
• Was the training data representative of these edge cases?

This investigative approach led to breakthrough improvements. By understanding why the model was making specific mistakes, we could refine prompts, adjust preprocessing, and provide clearer business context.

Lessons for QA Teams Entering GenAI

1. Embrace Ambiguity – Correctness isn’t absolute; help define it.
2. Think Like an Analyst – Validate business intent, not just outputs.
3. Build Structure Early – Logging and validation frameworks are essential.
4. Prioritize Meaningful Accuracy – Focus on impact, not just percentages.
5. Document Learnings – Capture edge cases and rules for future reuse.

The Bigger Picture: QA as AI Enablement: Our project achieved 90%+ accuracy across 41 of 42 entity types, processing 100-page documents in under 30 seconds. But the real win wasn’t just the metrics, it was creating a validation framework that turned AI uncertainty into business confidence.

As Gen AI expands, QA will demand hybrid skills: responsible for tracing logic, interpreting intent, and ensuring models aren’t just accurate, but meaningfully accurate.