LLM testing services Australia — large language model validation KiwiQA
LLM Testing & Validation · Australia & USA

LLM Testing Services
for Enterprise AI Applications.

KiwiQA's LLM testing practice validates large language model integrations across the dimensions traditional QA cannot reach — hallucination rate, OWASP LLM security, RAG accuracy, and EU AI Act conformity — with structured evaluation datasets and LLM-as-judge scoring.

LLM Testing Benchmarks
≤1%
Hallucination rate — high-risk
Top 10
OWASP LLM coverage
≥0.85
RAG Recall@k target
0.8–0.9
LLM-as-Judge correlation
200–500
Eval dataset Q&A pairs
EU Ready
AI Act Article 9/10/13/15
LLM Testing Coverage
Evaluation Dataset Construction200–500 pairs
Hallucination Rate MeasurementLLM-as-Judge
OWASP LLM Security TestingLLM01–LLM10
RAG Architecture ValidationRecall@k
Related services:AI TestingAI Security TestingPerformance TestingGet a QuoteLLM Validation Guide
The Problem

LLM applications fail in ways
your existing QA process cannot detect.

Standard Selenium/Playwright test automation passes every test while your LLM hallucinates confidently, leaks system prompts through indirect injection, or returns documents outside a user's access scope. Enterprise LLM deployment requires purpose-built validation.

Failure Modes Traditional QA Misses
✗
Non-deterministic outputs create false confidence
LLM outputs are probabilistic — binary pass/fail assertions run against one response provide no meaningful quality signal. Statistical evaluation across hundreds of calls is required.
✗
RAG retrieval failures cause silent hallucination
Retrieval failures silently return wrong document chunks — the primary cause of hallucination in enterprise knowledge bases. Retrieval recall@k is not measured by any standard test tool.
✗
Prompt injection bypasses system instructions
Prompt injection attacks (LLM01 in OWASP) override system prompts and extract confidential instructions. Indirect injection via retrieved documents is invisible to functional testing.
✗
Model provider updates change behaviour without code changes
GPT-4o, Claude Sonnet, and Gemini updates alter model behaviour without any change to your application. Continuous evaluation datasets catch regressions automatically.
✗
Compliance requires documented testing evidence — not self-assessment
EU AI Act and the Australian AI Ethics Framework compliance requires auditable test evidence. Post-hoc self-assessment does not satisfy Article 9 risk management obligations.
LLM Failure Landscape
Standard QA cannot
measure what it
cannot observe.
Hallucination across eval dataset
Prompt injection via retrieval
RAG access control failures
Provider model version drift
LLM Risk Reality
78%
LLM apps vulnerable to prompt injection (KiwiQA 2024)
≤1%
Hallucination threshold — high-risk enterprise LLMs
LLM01
Most commonly exploited OWASP LLM vulnerability
2025
EU AI Act binding enforcement commencement
LLM Testing Services

Six testing disciplines.
One structured engagement.

Purpose-built LLM validation covering evaluation methodology, security, RAG architecture, performance, and regulatory compliance evidence.

01
Evaluation Dataset Construction
We build 200–500 Q&A pairs with known correct answers, covering the specific knowledge domain your LLM serves. Datasets include adversarial variants designed to surface hallucination and boundary failures.
200–500 curated pairs
02
Hallucination Rate Measurement
LLM-as-judge scoring correlated against human review (target 0.8–0.9 inter-rater correlation). We measure groundedness against retrieved context, factual accuracy against verified sources, and rate by risk tier.
High-risk ≤1% target
03
OWASP LLM Top 10 Security
Full assessment against the OWASP LLM Top 10: prompt injection (LLM01), insecure output handling (LLM02), sensitive information disclosure (LLM06), and indirect injection via document retrieval.
100% LLM01–LLM10
04
RAG Architecture Validation
Retrieval recall@k measurement, chunk relevance scoring, groundedness evaluation, and access control verification. We test whether your retrieval layer returns documents the querying user is authorised to see.
Recall@k ≥0.85
05
LLM Performance Testing
Latency distribution across percentiles (p50/p95/p99), token cost per query, and concurrent load testing to validate infrastructure headroom under enterprise query volumes.
p95 latency ≤2 000ms
06
Compliance Testing
Documented testing evidence for EU AI Act Article 9/10/13, Australia's Voluntary AI Ethics Framework, and US NIST AI RMF. Produces artefacts suitable for regulator and board-level review.
EU AI Act · NIST · AU
Evaluation Methodology

How we measure LLM quality
with statistical rigour.

Evaluation Dataset Construction

Every LLM testing engagement begins with a domain-specific evaluation dataset of 200–500 question-answer pairs with verified correct responses. The dataset covers the full scope of topics the LLM is expected to handle, including edge cases, boundary questions, and adversarial variants designed to surface hallucination pathways.

Dataset construction includes manual expert review and automated consistency checking to ensure ground truth labels are unambiguous and reproducible across evaluation runs.

LLM-as-Judge Scoring

We use a separate, independent LLM (configured as an evaluator with a structured scoring rubric) to assess each response for groundedness, factual accuracy, and relevance. Our LLM-as-judge configurations achieve 0.8–0.9 inter-rater agreement with human expert reviewers — sufficient for enterprise quality gates.

All judge scores are validated against a human-labelled calibration set on each engagement to confirm correlation holds for the specific domain.

Hallucination Thresholds by Risk Tier

High-risk AI systems≤1% hallucination rate
Healthcare, legal, financial advisory, government
Medium-risk AI systems≤5% hallucination rate
Customer service, internal knowledge bases, HR
Low-risk AI systems≤10% hallucination rate
Content generation, marketing copy, internal tools
LLM Testing Tools
Custom Eval Harnesses
LLM-as-Judge
RAGAS Framework
DeepEval
Promptfoo
JMeter
Postman
Python pytest
OpenAI Evals
Langsmith
Evaluation KPITarget
Hallucination Rate (high-risk)≤1%
Hallucination Rate (medium-risk)≤5%
LLM-as-Judge Correlation0.8–0.9
RAG Recall@k≥0.85
Groundedness Score≥0.90
OWASP LLM CoverageLLM01–LLM10
LLM p95 Latency≤2 000ms
Token Cost Variance±15% of budget
Security

OWASP LLM Top 10.
The vulnerabilities that matter.

The OWASP LLM Top 10 defines the security vulnerabilities specific to large language model applications. KiwiQA tests your LLM against every item — with particular depth on the four highest-prevalence risks we encounter in enterprise deployments.

LLM01Prompt InjectionCritical

An attacker crafts inputs that override system instructions — instructing the LLM to ignore safety guardrails, reveal system prompt contents, or perform unauthorised actions. Direct injection targets user-supplied input; indirect injection embeds malicious instructions in retrieved documents, emails, or web pages that the LLM processes.

LLM02Insecure Output HandlingHigh

When LLM-generated text is passed to downstream systems (browsers, SQL engines, shell interpreters) without sanitisation, the output itself becomes an attack vector. A prompt injection that causes the LLM to emit JavaScript achieves XSS if the output is rendered in a browser without escaping.

LLM06Sensitive Information DisclosureHigh

LLMs trained on or given access to sensitive data can be induced to reproduce it — including training data, system prompt contents, API keys embedded in context, or documents retrieved from RAG stores outside the user's authorisation scope. We test extraction via direct query, indirect injection, and role-play scenarios.

LLM03Training Data PoisoningMedium

For systems where users can influence training or fine-tuning data, adversarial examples can be introduced to change model behaviour at inference time. We assess fine-tuning pipelines for data validation controls and test for known poisoning signatures in model outputs.

Client Experience

"We deployed a Claude-powered contract review platform. KiwiQA found that our RAG layer had no access controls — a crafted query could return contracts from other client accounts. They also found our system prompt was extractable via prompt injection. Neither was in our functional test plan."

— Head of AI Platform Engineering, US Legal Technology Company
LLM Testing Insights

Expert guides on
large language model validation.

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FAQ

Frequently asked questions

Everything you need to know — answered.

What is LLM testing and why is it different to standard software testing?
+

LLM testing validates non-deterministic, probabilistic outputs that cannot be evaluated with binary pass/fail assertions. It requires statistical evaluation across many calls, LLM-as-judge scoring, adversarial prompt libraries, and domain-specific evaluation datasets — none of which standard Selenium or Playwright frameworks provide.

How do you measure LLM hallucination rate?
+

We build an evaluation dataset of 200–500 questions with verified correct answers, then run the LLM across the full set multiple times. Each response is scored by an independent LLM judge for groundedness and factual accuracy, correlated against human review. We report hallucination rate per risk tier: high-risk applications target ≤1%, medium-risk ≤5%.

What is the OWASP LLM Top 10?
+

The OWASP LLM Top 10 is the authoritative list of security vulnerabilities specific to large language model applications. The highest-risk items include LLM01 (prompt injection — overriding system instructions), LLM02 (insecure output handling — LLM output used unsafely downstream), and LLM06 (sensitive information disclosure — leaking training data or system prompts).

How do you test a RAG application?
+

RAG testing covers three layers: retrieval (does the vector search return the right chunks at recall@k ≥0.85?), grounding (does the LLM response stay within the retrieved context?), and access control (does a user query only retrieve documents they are authorised to access?). Access control failures in RAG are the leading enterprise security risk we find.

What LLM testing is required for EU AI Act compliance?
+

High-risk AI systems under the EU AI Act require documented accuracy and robustness testing (Article 15), data governance evidence (Article 10), and transparency documentation (Article 13). For LLM applications this means evaluation dataset construction, hallucination rate reporting, and bias measurement — all as auditable test artefacts.

How is LLM performance testing different to standard API performance testing?
+

LLM endpoints have variable response times driven by token generation, not fixed compute paths. Performance testing must capture full latency distributions (p50/p95/p99), token cost per query, and the interaction between concurrent users and context window limits — patterns standard HTTP load testing tools do not model by default.

LLM Testing Services · Australia & USA

Your LLM goes to enterprise production.
We validate it gets there safely.

Evaluation dataset construction, hallucination measurement, OWASP LLM security, RAG validation — KiwiQA's LLM testing practice covers every dimension traditional QA cannot reach.

6
LLM testing dimensions
in every K-ASCI engagement
EU AI Act aligned · OWASP LLM Top 10 · 24-hour response