How I, as a QA, Still Write Documentation but with AI I Spend Hours Instead of Days

Introduction

For years, quality assurance (QA) professionals have wrestled with a paradox: the more thorough the testing, the more documentation piles up. Test plans, test cases, bug reports, and release notes are essential for traceability and collaboration, but they also consume days of manual work. A recent experience shared by a senior QA engineer at Cloud.ru illustrates a turning point. By integrating AI assistants into their documentation workflow, they cut creation time from days to hours—without sacrificing quality. This case study breaks down the problem, the AI-driven solution, the measurable results, and the lessons learned for any QA team looking to modernize.

The original article (in Russian) was published on Habr in July 2026 and describes a practical journey that many testers will recognize. Source

The Problem: Documentation as a Bottleneck

Every QA engineer knows the drill: after exploratory testing, you must formalize findings. A typical sprint might require:

• Test cases – 50 to 200 steps covering functional, UI, and edge scenarios.
• Bug reports – detailed reproduction steps, expected vs. actual results, environment details.
• Test summary reports – statistics, coverage gaps, risks.

Creating these artifacts manually takes a seasoned QA engineer two to three full working days per sprint. The engineer in the Cloud.ru article reported spending roughly 40% of their workweek on pure documentation, leaving less time for actual testing, automation, or exploratory sessions.

Moreover, consistency suffers. Different team members use different phrasing, levels of detail, and template adherence. The result? Rework during peer reviews and confusion for developers who consume those documents.

The core problem was not laziness—it was the repetitive, structured nature of writing that AI excels at.

The Solution: AI as a Documentation Copilot

The QA engineer decided to change their workflow by leveraging a large language model (LLM) accessible via a company‑sanctioned API. Instead of writing from scratch, they adopted a prompt‑engineering approach:

1. Structured input templates – They created a Markdown template for test cases with placeholders for preconditions, test steps, expected results, and severity.
2. Context injection – Before asking the AI to generate text, they fed it the relevant user story, acceptance criteria, and a few examples of well‑written test cases from the same project.
3. Iterative refinement – The initial AI output was reviewed, corrected, and then used as a baseline. For subsequent similar features, the AI learned the style after two or three corrections.

The AI was not left unsupervised. Every generated piece was manually verified—especially accuracy of expected results—because hallucinated steps could lead to false positives.

Tools Used

The engineer used a combination of:
- An in‑house LLM proxy (based on OpenAI’s GPT‑4o) – the company’s own secure instance to avoid data leaks.
- Standard markdown editors (VS Code with a preview plugin) – to quickly format and export to their test management system (TestRail).

ASI Biont supports connecting to various project management and testing tools through its API—learn more at asibiont.com/courses. This integration capability shows how AI‑powered documentation fits into existing QA pipelines without forcing a platform switch.

The Results: From Days to Hours

After adopting the AI‑assisted workflow, the engineer tracked time spent on documentation over six sprints. The improvements were dramatic:

The quality of the output, measured by peer review feedback, remained consistent. In fact, the AI‑generated documents had fewer grammatical errors and more uniform formatting than manually written ones. The engineer noted that the time saved was reinvested into deeper test analysis, automation script improvements, and investigating edge cases that previously went unexplored.

Key Takeaways for QA Teams

1. AI does not replace QA; it replaces the drudgery. The critical thinking, domain knowledge, and validation skills of a QA engineer are still required. The AI is a tool to amplify human effort.
2. Prompt engineering matters. Garbage in, garbage out. The engineer spent two days crafting effective prompts that included context, examples, and explicit formatting instructions. This upfront investment paid off quickly.
3. Security first. Using a public chatbot for sensitive project data is a risk. The solution used a private, company‑controlled model instance. Teams should always ensure compliance with data protection policies.
4. Iterate and train. The AI improved after each correction. Storing successful prompts in a shared „prompt library” helps the whole team benefit from accumulated experience.
5. Don’t skip reviews. Every AI‑generated document must be reviewed by a human before being published. The risk of hallucinations is real, especially in complex business logic scenarios.

Conclusion

The experience of the Cloud.ru QA engineer is not an outlier—it’s a glimpse into the standard practice that will likely become the norm in QA departments within a few years. By embracing AI as a documentation copilot, they reclaimed 80% of the time previously lost to manual writing. The key was not to automate entirely, but to augment the human expert so they can focus on what truly adds value: uncovering bugs, improving test coverage, and ensuring product quality.

For any QA professional who feels buried under documentation, the message is clear: adopt AI assistance, but keep your critical judgment sharp. The hours you save will be the days your team deserves.

Read the full original story on Habr: https://habr.com/ru/companies/cloud_ru/articles/1062556/