{"id":18887,"date":"2026-06-03T15:23:28","date_gmt":"2026-06-03T12:23:28","guid":{"rendered":"https:\/\/mastersoftesting.com\/%d8%a7%d8%ae%d8%aa%d8%a8%d8%a7%d8%b1-%d8%a7%d9%84%d8%b0%d9%83%d8%a7%d8%a1-%d8%a7%d9%84%d8%a7%d8%b5%d8%b7%d9%86%d8%a7%d8%b9%d9%8a-%d9%88%d9%86%d9%85%d8%a7%d8%b0%d8%ac-%d8%a7%d9%84%d9%84%d8%ba%d8%a9\/"},"modified":"2026-06-03T16:45:44","modified_gmt":"2026-06-03T13:45:44","slug":"ai-llm-testing-services","status":"publish","type":"page","link":"https:\/\/mastersoftesting.com\/ar\/ai-llm-testing-services\/","title":{"rendered":"\u0627\u062e\u062a\u0628\u0627\u0631 \u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a \u0648\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0644\u063a\u0629"},"content":{"rendered":"

Ship AI features your users can actually trust.<\/em><\/p>

AI doesn\u2019t fail like normal software. A model can give a different answer every time, hallucinate facts with total confidence, leak data through a clever prompt, or quietly degrade the moment you change a prompt or swap a model. We test LLMs, chatbots, copilots, RAG systems, and AI-powered products for accuracy, safety, and reliability \u2014 and we build the evaluation pipelines that keep them dependable as they evolve. Whatever your AI needs, we implement testing around it.<\/p>\n

\n\"Make<\/span><\/span><\/span><\/a>\n<\/div>\n

Why AI needs a different kind of testing<\/h2>
    \n
  • Non-deterministic outputs<\/strong> \u2014 the same input can produce different results<\/li>\n
  • Hallucinations<\/strong> \u2014 confident, plausible, and completely wrong<\/li>\n
  • Prompt injection & jailbreaks<\/strong> \u2014 an entirely new security attack surface<\/li>\n
  • Bias, toxicity & safety risks<\/strong> \u2014 reputational and compliance landmines<\/li>\n
  • Silent regressions<\/strong> \u2014 quality drifts when you tweak a prompt or change models<\/li>\n
  • \u201cQuality\u201d is hard to define<\/strong> \u2014 what does a good<\/em> answer even mean?<\/li>\n<\/ul>

    What we test<\/h2>
      \n
    • LLM Output Quality & Accuracy<\/strong> \u2014 graded against expected behavior with evals and golden datasets<\/li>\n
    • Hallucination & Factuality Testing<\/strong> \u2014 catch confident-but-wrong answers before users do<\/li>\n
    • RAG & Retrieval Accuracy<\/strong> \u2014 is the model genuinely grounded in your data?<\/li>\n
    • Prompt Injection & AI Red-Teaming<\/strong> \u2014 adversarial testing for jailbreaks and data leaks<\/li>\n
    • Bias, Toxicity & Safety<\/strong> \u2014 fairness checks and guardrail validation<\/li>\n
    • Regression Testing for Prompts & Models<\/strong> \u2014 catch quality drift whenever anything changes<\/li>\n
    • Performance, Latency & Cost<\/strong> \u2014 token usage, response time, and throughput under load<\/li>\n
    • Conversational & UX Testing<\/strong> \u2014 does the assistant actually help real users?<\/li>\n<\/ul>

      How we test AI<\/h2>
        \n
      • Evaluation suites (evals)<\/strong> built on golden datasets with automated scoring<\/li>\n
      • LLM-as-judge plus human review<\/strong> for the nuanced quality machines miss<\/li>\n
      • Adversarial red-teaming<\/strong> for safety, jailbreaks, and data-leak risks<\/li>\n
      • CI\/CD integration<\/strong> so every prompt and model change is checked before it ships<\/li>\n
      • Dashboards & metrics<\/strong> that turn \u201cAI quality\u201d into something you can actually see and track<\/li>\n<\/ul>

        Whether you\u2019re building a customer-facing chatbot, an internal copilot, a RAG system, or an agentic workflow, we tailor the testing approach to your product, your risks, and your stack.<\/p>

        Why teams trust Masters of Testing<\/h2>
          \n
        • Engineers fluent in modern AI stacks \u2014 LLMs, RAG, agents, and the tools around them<\/li>\n
        • Transparent, real-time reporting \u2014 no black boxes<\/li>\n
        • Secure practices and signed NDAs to protect your models, prompts, and data<\/li>\n
        • Trilingual delivery (English, Russian, Arabic) for global teams<\/li>\n\n<\/ul>

          Frequently asked questions<\/h2>

          Why can\u2019t I test AI with normal QA?<\/h3>

          Traditional tests expect the same input to give the same output. AI is non-deterministic and open-ended, so it needs evaluation-based testing \u2014 scoring quality, accuracy, and safety across many cases \u2014 rather than simple pass\/fail assertions.<\/p>

          How do you test something non-deterministic?<\/h3>

          We build evals: curated datasets of inputs with expected behavior, scored automatically (and with LLM-as-judge plus human review) so you get a measurable quality signal even when exact outputs vary.<\/p>

          Can you catch hallucinations?<\/h3>

          Yes \u2014 factuality and grounding tests flag confident-but-wrong answers, and for RAG systems we verify the model is actually using your retrieved data rather than inventing it.<\/p>

          Do you do AI security testing?<\/h3>

          We run adversarial red-teaming for prompt injection, jailbreaks, and data-leak risks, and validate that your guardrails actually hold under pressure.<\/p>

          Which models and stacks do you work with?<\/h3>

          The major hosted models and open-source ones alike, plus the surrounding stack \u2014 RAG pipelines, vector databases, agent frameworks, and your own fine-tuned models.<\/p>

          Can you integrate AI testing into our CI\/CD?<\/h3>

          Yes. We wire evals into your pipeline so every prompt, model, or config change is automatically checked for quality and safety regressions before it ships.<\/p>

          How do we measure AI quality over time?<\/h3>

          Through clear, tracked metrics \u2014 accuracy, hallucination rate, safety pass rate, latency, and cost \u2014 reported on dashboards so quality trends are always visible.<\/p>

          We\u2019re just starting with AI \u2014 can you help early?<\/h3>

          Absolutely. The earlier we set up evaluation and guardrails, the cheaper quality is to maintain. We\u2019ll help you build the right testing foundation from day one.<\/p>

          Ready to make your AI dependable?<\/h2>

          Let\u2019s talk about your AI product and where the risks are. It\u2019s free, and the whole conversation is about you.<\/p>

          \u0627\u062d\u0635\u0644 \u0639\u0644\u0649 \u062a\u0642\u064a\u064a\u0645 \u0645\u062c\u0627\u0646\u064a \u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a<\/h3>

          \u0623\u062e\u0628\u0631\u0646\u0627 \u0623\u064a\u0646 \u064a\u0642\u0641 \u0645\u0646\u062a\u062c\u0643 \u0627\u0644\u0630\u0643\u064a \u0648\u0633\u0646\u0631\u0633\u0644 \u062a\u0648\u0635\u064a\u0627\u062a \u0645\u062e\u0635\u0651\u0635\u0629 \u0644\u0643.<\/p>