Service
AI & LLM Testing
Specialised testing for AI-powered products — chatbots, RAG systems, and LLM integrations. Verify accuracy, speed, and user experience before your users find the issues.
Our engineers have tested a ChatGPT-powered patient chat system at Bupa — verifying response accuracy against a RAG knowledge base, latency, and UI quality. We bring that experience to your AI product.
What makes AI testing different
AI products fail probabilistically — a response can look correct, read naturally, and be completely wrong. Standard functional QA misses this entirely.
Accuracy Against Source Material
For RAG systems, we verify responses are grounded in the provided knowledge base — not hallucinated beyond it.
Hallucination Detection
We systematically test for responses that deviate from the knowledge base, including adversarial prompt injection attempts.
Latency Benchmarking
We define and test acceptable response-time thresholds for your use case — real-time support, healthcare, or asynchronous processing.
Regression Prompt Library
We deliver a curated prompt corpus you can run against every model update to catch quality regressions automatically.
What we test
AI products fail differently from traditional software. An LLM can return plausible-sounding but factually wrong answers, retrieve irrelevant documents from a RAG knowledge base, or respond too slowly for a real-time support context. Our engineers have tested AI chat systems in production at enterprise scale — including a Bupa patient-facing ChatGPT integration — and know exactly what to look for. We test accuracy relative to provided knowledge sources, response latency, fallback behaviour, and UI intuitiveness.
- Response Accuracy Testing
- Hallucination Detection
- RAG Knowledge Base Validation
- Latency & Performance Checks
- UI/UX Review
- Regression After Model Updates
What you get
- Accuracy audit: responses verified against knowledge base sources
- Hallucination report: cases where the model deviates from source material
- Latency benchmarks and acceptable response-time thresholds
- Edge-case prompt library for ongoing regression testing
- UX evaluation with actionable improvement recommendations
Read how we tested a ChatGPT system at Bupa → Bupa case study
Frequently Asked Questions
- AI products fail probabilistically — an LLM can return an answer that looks correct, reads fluently, and is completely wrong. Standard functional QA scripts check for expected outputs. AI testing requires verifying responses against source material, testing for hallucinations, adversarial prompting, and assessing latency under various load conditions.
- RAG (Retrieval-Augmented Generation) is a technique where an LLM is grounded against a specific knowledge base — for example, a company's documentation or product data. We verify that responses are accurate relative to that knowledge base, that the system does not invent information beyond what was provided, and that retrieval is working correctly.
- We can test any LLM integration regardless of the underlying model — GPT-4, Claude, Gemini, Llama, Mistral, or custom fine-tuned models. What we test is the behaviour of the system as a whole, including the retrieval layer, the prompt engineering, and the user interface.
- You receive an accuracy audit (responses verified against source material), a hallucination report, latency benchmarks, a curated regression prompt library you can run after every model update, and UX recommendations for the chat interface.
What makes AI testing different from standard software testing?
What is a RAG system and how do you test it?
Can you test any AI model or just ChatGPT?
What do we receive at the end of an AI testing engagement?
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