Standard QA can't catch hallucinations. Selenium won't tell you if your RAG pipeline retrieves the wrong context. We test what traditional QA misses — before your customers find it.
Standard QA techniques don't work for AI-powered software. Unit tests can't catch hallucinations. Your CI/CD pipeline has no idea how to evaluate whether a language model's output is factually correct, safe, or consistent.
LLM testing is the practice of systematically evaluating the behavior, accuracy, safety, and reliability of applications built on large language models. Unlike traditional software testing — which checks deterministic inputs and outputs — LLM testing deals with probabilistic systems where the same prompt can produce different responses, and where failures manifest as subtle inaccuracies rather than hard errors. LLM testing covers hallucination detection, output consistency measurement, prompt injection security, contextual relevance evaluation, and performance under load. For SaaS teams building AI-powered products, LLM testing is not optional — it is the difference between a product that earns user trust and one that erodes it.
We measure whether your model produces accurate, consistent, and contextually appropriate responses — across different prompts, edge cases, and adversarial inputs. We quantify hallucination rates, output drift, and performance degradation over time.
For retrieval-augmented generation systems, we test the full pipeline: document ingestion accuracy, embedding quality, retrieval relevance, context assembly, and final output coherence. A RAG system that retrieves the wrong documents will answer confidently — and incorrectly.
Autonomous agents that call external tools, make decisions, or operate with elevated permissions require specific testing for scope creep, unexpected behavior chains, and failure recovery. We define acceptance criteria for AI agents and test systematically.
LLM-powered applications have a new attack surface: the prompt itself. We test for prompt injection vulnerabilities, jailbreak susceptibility, data leakage through model outputs, and unauthorized instruction following — covering the OWASP LLM Top 10.
For AI products in regulated sectors (HR tech, fintech, healthcare SaaS), we test for systematic bias in model outputs and document findings in a format that supports GDPR and EU AI Act compliance requirements.
We establish baseline performance metrics for your AI features under realistic load — because a feature that works perfectly in demo conditions may degrade significantly when 500 concurrent users hit it.
Testing a retrieval-augmented generation (RAG) system requires evaluating three independent layers: retrieval quality, context assembly, and generation accuracy.
Retrieval quality testing checks whether the right documents are retrieved for a given query — measuring precision, recall, and ranking relevance against a labeled test set.
Context assembly testing verifies that retrieved chunks are correctly formatted and passed to the model without truncation or corruption — a subtle failure that causes confident but wrong answers.
Generation accuracy testing evaluates whether the model produces responses that are grounded in the retrieved context, free of hallucination, and consistent across equivalent queries.
At QualityArk, we build custom RAG evaluation pipelines that combine automated metric scoring with human-in-the-loop review for edge cases — giving SaaS teams a repeatable, measurable quality bar for their AI systems.
A focused, time-boxed testing engagement for AI-powered SaaS products. Structured, fast, fixed-price.
SaaS companies that have built or are building an AI-powered feature — a chatbot, intelligent search, AI-assisted workflow, or autonomous agent — and need to validate it before launch or a major customer demo.
LLM security vulnerabilities are distinct from traditional software vulnerabilities and require specialized testing techniques. The primary attack vectors include: prompt injection, indirect prompt injection, jailbreaking, data leakage, and excessive agency in autonomous agents.
Finding LLM security vulnerabilities requires a combination of red-teaming (adversarial manual testing), automated fuzzing with adversarial prompt libraries, architecture review to identify over-permissioned tool calls, and output monitoring to detect anomalous responses. QualityArk conducts LLM security assessments covering the OWASP Top 10 for LLM Applications alongside custom threat modeling for each client's specific architecture.
Book a free 30-minute discovery call. We'll review your AI architecture and tell you exactly what needs to be tested before you ship.
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