Daria Tsion
Daria Tsion
Head of QA
she/her
Head of QA with 11+ years of experience in building QA processes, leading teams, and driving test automation. Passionate about AI in QA, process optimization, and leadership in quality.
🎂 MoTaBirthday | August 19, 2025
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The end of 2025 was emotionally one of the hardest periods for me since 2022. Living in a world full of political changes while war is happening in your own country is not easy. Blackouts, massive ...
28 Jan
Feature flags entered our workflow as a quality safeguard.
14 Apr
Use these structured prompting techniques to improve the quality and usefulness of AI output in testing workflows
20 Jan
Set meaningful goals, communicate quality through risks and real-world consequences, and turn small wins like building a quality narrative into career growth.
30 Jan
Feature Flag
At a basic level, a feature flag acts as a conditional switch. The code for a feature can be deployed to production or any other environment, but its visibility or behaviour is controlled by configuration in a specific system. By changing the flag’s state, teams can turn the feature on or off instantly, often for specific users, environments, or percentage-based rollouts. This can make the testing process much clearer and easier.
Test surface
The test surface in feature testing represents the total, combined area of all public methods, parameters, and application programming interfaces (APIs) of a component that must be validated to ensure it works correctly. It defines the scope of testing required, where a larger, more complex surface area necessitates more in-depth testing. It includes all possible variations introduced by factors such as feature flags, environment differences (e.g. dev, staging, production), user segments, and rollout strategies.Understanding and managing the test surface is important for effective test planning, as it helps teams identify what needs to be tested, avoid gaps in coverage, and reduce the risk of issues caused by untested combinations of conditions
Prompt chaining
Prompt chaining is a technique where the output of one prompt is used as the input for the next prompt in a sequence. This allows complex tasks to be broken into smaller, more manageable steps, enabling deeper analysis, comparison, and refinement across large or complex problem spaces.
Self-critique prompting
Self-critique prompting describes the practice of asking AI to review its own output against specific criteria, such as readability, coverage, or standards, and then improve the result based on those findings. This mirrors human review processes and helps identify gaps, inconsistencies, or improvement opportunities before the output is used in production.
Iterative prompting
Iterative prompting is an approach where prompts are refined over multiple steps based on previous outputs. Instead of expecting a perfect result from a single prompt, the user reviews, adjusts constraints, and asks follow-up questions to gradually improve accuracy, quality, and relevance.
Comments
(7)
Testing with feature flags: what we expected and what actually happened
Thank Brandy, for sharing your experience. That's such a classic feature flag story 😄
They add a layer of complexity that’s easy to underestimate and manage sometimes. And from my experience, such "unrelated flag" bugs usually come from FF dependencies in the system, which is also another topic to discuss.
Did you have any strategies for managing or testing flag combinations? Would love to hear!
Testing with feature flags: what we expected and what actually happened
Thanks so much, Gary! 🙏
Feature flags can feel a bit "extra" at first. Still, once you start using them intentionally (for safer releases and experiments), they really change how you think about delivery.
Curious if you’re considering introducing them in your workflow anytime soon?
Five practical ways to use AI as a partner in Quality Engineering
@Gary, thanks so much for your kind words and feedback! I'm glad you have found something useful in my milestone!
I'm an Ambassador now
Congrats! Well deserved!
Five practical ways to use AI as a partner in Quality Engineering
Thank you, Pranav! This is a great way to frame it.
That shift from execution to orchestration is exactly what I’ve been observing in practice. The more AI takes over repetitive or mechanical tasks, the more QA value moves into problem framing, defining constraints, and sense-making of the output.
For me, prompting became less about “getting code faster” and more about making my own thinking explicit, especially around risks and assumptions. I’m glad that part came through in the article.
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Testing with feature flags: what we expected and what actually happened
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Testing with feature flags: what we expected and what actually happened
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