LLM output consistency, hallucination detection, prompt-injection resistance, agent reliability, and evaluation methodology — built for teams shipping AI-powered products.
AI application testing verifies that AI-powered features — large language models, AI agents, recommendation engines, and generative outputs — behave reliably, consistently, and safely under real-world conditions. Traditional QA checks whether software does what it's supposed to do. AI testing goes further: it checks whether the AI gives the right answer, whether that answer is consistent across rephrasings, whether the system resists adversarial inputs, and whether hallucinated or fabricated content reaches users. Gil Product Studio builds and tests AI products, so our testing methodology is grounded in actual production experience with LLMs and agents — not just theory.
Each area maps to a real failure mode we've seen in shipped AI products. We test for all of them.
We test the same prompt across multiple runs, rephrasings, and input variations to measure output stability. Inconsistent outputs erode user trust fast.
We compare AI outputs against known ground truth data, flag confident-but-wrong responses, and measure hallucination rates across prompt categories.
We test agents for decision consistency, action routing accuracy, guardrail adherence, recovery from unexpected inputs, and correct escalation behavior.
We test whether users can manipulate the AI into ignoring instructions, leaking system prompts, or producing unintended outputs through crafted inputs.
We build evaluation frameworks that run automatically against your prompts, measure quality over time, and give your team a repeatable way to compare model versions and prompt changes.
Empty inputs, extremely long inputs, multilingual inputs, code-switching, adversarial formatting — we find the edges where the AI falls over.
We adapt our standard QA process for AI-specific evaluation.
AI application testing verifies that AI-powered features — LLMs, agents, recommendation engines, and generative outputs — behave reliably, consistently, and safely under real-world conditions. It covers output consistency, hallucination detection, prompt-injection resistance, and edge-case handling.
We evaluate LLM outputs across multiple dimensions: consistency across rephrasings, factual accuracy against source data, hallucination rate, prompt-injection resistance, tone and formatting stability, and edge-case behavior. We build evaluation frameworks that run automatically against your prompts and input variations.
Hallucination testing checks whether an AI system generates false, fabricated, or unverifiable claims. We design test sets that compare AI outputs against known ground truth, flag confident-but-wrong responses, and measure hallucination rates across prompt categories.
Yes. We test AI agents for decision consistency, action routing accuracy, guardrail adherence, recovery from unexpected inputs, and behavior under adversarial or edge-case conditions. We verify that agents stay within their intended scope and escalate correctly when they should.
Start with a QA Audit — fixed price, one week, plain-language report.