AI QA Engineer – Digital Tester for Your Team

AI QA Engineer — Digital Tester for Your Team

Testing grows faster than the team: every PR brings dozens of changes, and QA can't even cover the critical path. Coverage drops, regressions slip into production. We solve this differently—we introduce an AI QA Engineer, a digital employee that automates test case generation, automated test writing, failed test analysis, and report creation. It integrates into your CI pipeline and works as a full team member, reducing routine workload by 55%.

Our experience in test automation exceeds 5 years; we've delivered over 50 projects in fintech, e-commerce, and SaaS. We guarantee the AI QA Engineer pays for itself within 3 months by cutting regression testing time and raising coverage to 80%+. Average QA budget savings range from 500k to 2M RUB per year depending on team size.

How AI QA Engineer Accelerates Coverage

The foundation is an LLM (GPT-4o, Claude 3.5) with a RAG pipeline for accessing your codebase and test history. The model generates test cases following IEEE 829, immediately splitting them into positive, negative, boundary, and security checks. Test data is always concrete: not "test data" but valid JSON objects, SQL queries, or API responses.

from openai import AsyncOpenAI
from pydantic import BaseModel
from typing import Literal

client = AsyncOpenAI()

class TestCase(BaseModel):
    id: str
    title: str
    category: Literal["positive", "negative", "edge_case", "security", "performance"]
    preconditions: list[str]
    steps: list[str]
    expected_result: str
    priority: Literal["critical", "high", "medium", "low"]
    test_data: dict

async def generate_test_cases(
    feature_description: str,
    acceptance_criteria: list[str],
    existing_test_cases: list[str] = None,
) -> list[TestCase]:

    existing_context = f"\nAlready existing test cases (do not duplicate):\n{chr(10).join(existing_test_cases[:10])}" if existing_test_cases else ""

    response = await client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[{
            "role": "system",
            "content": f"""You are a QA engineer with 8 years of experience.
Create test cases per IEEE 829.
Must include: happy path, boundary values, negative scenarios, security.
Test data must be concrete (not 'test data').{existing_context}"""
        }, {
            "role": "user",
            "content": f"""Feature: {feature_description}
Acceptance criteria:
{chr(10).join(f'- {ac}' for ac in acceptance_criteria)}"""
        }],
        response_format=list[TestCase],
        temperature=0.3,
    )

    return response.choices[0].message.parsed

Why Automated Test Generation Is More Effective Than Manual

Compare: manual test case writing takes an average of 20–30 minutes per case, while AI generates 5–10 cases in seconds. But the key is quality—the model doesn't forget an edge case you might miss. It analyzes failure history and avoids repeating flaky tests. The AI QA Engineer also performs defect analysis by correlating failures with history and uses an LLM QA model for deep understanding of testing logic.

How the AI QA Engineer Works: Step-by-Step Process

1. Analyze code changes. On each PR, the system extracts the diff, identifies changed files, and determines affected areas.
2. Generate test cases. Using the diff and context, the LLM creates a set of test cases including edge cases.
3. Automatically write automated tests. Generated cases are translated into pytest (API) or Playwright (E2E), using existing fixtures and Page Object Model.
4. Run in CI and analyze results. Tests execute in the pipeline; the failed test analyzer identifies flaky tests and root causes.
5. Generate coverage report. The system calculates code coverage and highlights priority uncovered areas.

What's Included When We Implement an AI QA Engineer

We deliver a ready-made turnkey solution:

• Test case generation module – integration with your requirements system (Jira, Notion, Confluence).
• Automated test generator – writes pytest for API and Playwright for E2E, supporting Page Object Model and existing fixtures.
• Failed test analyzer – integrates with CI (GitLab CI, Jenkins, GitHub Actions) for automatic root cause analysis and fix suggestions.
• Coverage reporting – weekly reports with priorities for uncovered critical paths.
• Team training – 2 sessions on working with the AI QA Engineer.
• Guarantee – 1 month of support after launch.

How We Do It: Stack and Process

Stack: OpenAI GPT-4o, Hugging Face Transformers, LangChain, ChromaDB (for RAG over test history), PyTorch, MLflow for metric tracking. Deployment via Docker into your Kubernetes or SageMaker.

Implementation phases:

Real Case: Fintech Project with 3 QA for 8 Developers

Situation: The QA team couldn't keep up with testing all outgoing code. Coverage was 51%, test debt was accumulating. Each release had 2–3 regressions in production. We implemented the AI QA Engineer.

How it worked:

• When a PR was opened, the system automatically generated test cases from the diff.
• For new API endpoints, pytest tests were generated.
• In CI, the failed test analyzer identified flaky tests (23 were marked) and suggested specific fixes.
• A weekly coverage report with priorities was generated.

Results after 3 months:

• Test coverage: 51% → 79%
• Time spent writing tests reduced by 55%
• Regression detection before production: +34%
• The QA team shifted to exploratory testing and code review.

Implementation Timeline

• Test case generator from requirements: 1–2 weeks
• Automated pytest/Playwright test generation: 2–3 weeks
• Failed test analyzer + CI integration: 1–2 weeks
• Coverage reporting: 1 week
• Total: 5–8 weeks to full operation

If you want to estimate savings for your project, get a consultation—we will conduct a free test coverage audit in 2 days. Contact us for a payback calculation.

OpenAI API documentation

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.