Why Prompt Engineering Will Become a QA Skill - Analytical Perspective — Dmytro Kyiashko, AI-Focused Software Developer in Test

Traditional software testing follows simple logic: input X produces output Y. That predictability enables automation and lets QA teams verify code works as intended. Large language models break this pattern. The same prompt can generate different responses depending on conversation history or context. For QA engineers trained on deterministic systems, this creates an uncomfortable question: how do you test something that refuses to behave consistently?

Dmytro Kyiashko encountered this problem while building test frameworks for AI assistants at Xenoss, a U.S.-based AdTech technology company that develops scalable solutions for automation and AI testing. Xenoss made the Inc. 5000 list of America’s fastest-growing private companies, won AI Company of the Year at the UK Business Tech Awards, and appeared among TechReviewer’s Top 100 IoT Development Companies in the USA, all in 2025. His team ensured these systems behaved reliably across thousands of user interactions. Early test suites revealed something unexpected—models would drift after processing several hundred prompts, not from code errors but because they had quietly shifted how they interpreted core instructions. Traditional testing infrastructure wasn’t built to catch this.

When Models Decide to Reinterpret Instructions

Kyiashko has spent nearly a decade in software development and testing, including more than four years managing QA teams and six years building them from scratch. When he shifted toward AI-based testing several years ago, he assumed the fundamentals would transfer. They didn’t.

Behavioral drift became the first major obstacle. A model would process thousands of prompts correctly, then quietly change how it handled instructions. No errors appeared in the logs — just a gradual shift that traditional tools never flagged. His team discovered an AI assistant had started interpreting “urgent” requests differently after extended dialogue—not randomly, but with its own internal logic that evolved during operation.

This forced a complete rethink. Instead of verifying inputs produced expected outputs, they tracked behavioral consistency across hundreds of interactions. Could the model maintain the same interpretation of priority levels throughout long conversations? Did it handle ambiguous phrasing consistently? These questions don’t have binary answers, making traditional automation frameworks nearly useless.

Building Test Suites from Conversations

Kyiashko’s solution was treating prompts like test cases. His team writes thousands of prompt variations per sprint, each exposing specific failure modes. The work resembles traditional test design but requires different thinking—instead of checking function outputs, testers evaluate whether responses demonstrate appropriate reasoning given ambiguous information.

An AI system handling enterprise workflows might process straightforward questions perfectly but fail when someone frames the same request as a complaint. Kyiashko’s test suites introduce deliberate ambiguity, switch contexts mid-conversation, and inject cultural references models might misinterpret. Each variation reveals whether the system responds consistently or improvises its own rules.

One revealing pattern involves extended dialogues where models must maintain context over dozens of turns. Kyiashko’s frameworks include scenarios where users return to topics mentioned twenty exchanges back, testing whether the model truly maintains memory or just mimics it.

Metrics That Actually Matter

Standard AI benchmarks test what models can do rather than what they actually do under pressure, according to Kyiashko. His team developed evaluation frameworks accounting for dimensions traditional metrics ignore: does the response maintain appropriate tone, address the user’s actual intent, avoid stating information confidently when it should express uncertainty?

Kyiashko also applies the “LLM-as-a-Judge” approach — a testing method where one model evaluates another. His team builds datasets of prompts and responses generated by the primary AI system, then uses a separate language model to assess whether each response aligns with the intended prompt. This secondary evaluation provides scalable, automated quality scoring without requiring continuous human supervision.

Human evaluation remains essential. Automated metrics catch obvious failures but struggle with nuance. His frameworks combine automated checks with structured human review, where evaluators assess responses across multiple dimensions—fluency, relevance, logical coherence, contextual appropriateness. Human review is expensive and limits testing thoroughness before deployment.

Testing Autonomous Decision-Making

Testing agentic systems requires evaluating entire reasoning chains, not just final outputs. A model might reach the correct conclusion through flawed reasoning, or demonstrate sound logic while missing crucial context. Kyiashko’s test frameworks for these systems capture intermediate steps, evaluate whether models consider appropriate alternatives, and verify that they recognize when they lack sufficient information to proceed confidently.

Companies integrating AI into large-scale AdTech and automation platforms face particularly high stakes with agentic systems. A model that occasionally produces unsuitable recommendations not only generates incorrect outputs but erodes user trust and creates operational liability. Testing frameworks in these domains need to verify that models degrade gracefully when facing edge cases or genuinely ambiguous situations.

Building Frameworks That Scale

The evaluation pipeline Kyiashko developed for enterprises integrating AI into data-heavy production environments combines multiple testing approaches. Statistical analysis catches performance degradation. Prompt variation testing exposes inconsistencies. Expert review loops verify domain-specific quality standards. The methodology treats prompts like formal test specifications, with the same rigor applied to API contracts. This creates traceability—something regulators increasingly demand from organizations deploying AI in sensitive domains.

Kyiashko is a member of IEEE, the world’s largest professional association for engineers and researchers in computer science, telecommunications, and AI. He has served as a reviewer and evaluator for scientific papers presented at international conferences, including The World Conference on Emerging Science, Innovation and Policy and the International Conference on Next-Generation Innovations and Sustainability, providing expert feedback on studies related to AI system evaluation. He was an expert judge at UAtech Venture Night during Web Summit Vancouver, evaluating Ukrainian startups presented to international investors and technical leaders.

He also works as a Software Developer in Test at a U.S.-based digital title and settlement technology company.

He has authored a practical guide to automated evaluation of AI systems. The book outlines the architecture, quality metrics, and processes that have proven effective in real production environments. It is written for professionals who need solutions that work, not just theory.

What This Means for QA Teams

Enterprises are hiring QA engineers with LLM evaluation experience—skills that barely existed two years ago. Kyiashko sees testing moving away from validating static results toward evaluating dynamic intelligence.

The organizations building proper testing capabilities now won’t deal with reliability problems later. Comprehensive prompt-based test suites take time to develop, but insufficient testing costs more. Models that drift or fail unpredictably undermine business value.

Prompt engineering is becoming the foundational language for testing AI behavior. QA professionals who master it are the ones enterprises want.

About the author

Michael Reeves is a technology writer specializing in artificial intelligence quality assurance, software testing, and enterprise automation. He writes analytical pieces on how large language models are reshaping QA practices, with a focus on prompt engineering, AI reliability, and scalable evaluation frameworks used in real-world production systems.