LLM & RAG TESTING
Your RAG Pipeline Has Blind Spots. We Find Them
Most teams discover LLM and RAG failures after users do. We run structured evaluation across your entire pipeline, covering hallucination, retrieval precision, faithfulness, contextual recall, prompt robustness, and safety, and deliver a clear picture of where your system fails, why it fails, and what to fix first.
THE PROBLEM
Standard QA misses the failures that matter.
Each one reaches production undetected. None show up in a standard test report.
01
Hallucination passes as correct
Your pipeline returns a confident, well-formatted answer. Standard tests pass. The answer is fabricated. Without LLM-specific evaluation, there is no signal. Just a user who has already acted on it.
02
The right chunks. The wrong answer
Retrieval fires. Recall looks healthy. But what gets assembled is not answering the query. It is close enough to fool the pipeline, not close enough to help the user. The model fills the gap. You find out when someone complains.
03
Adversarial inputs. Zero coverage
Prompt injection, jailbreak attempts, boundary conditions. Your test suite was not written for these because standard QA is not designed to think adversarially. LLMs fail at the edges. That is exactly where attackers and unlucky users end up."
ENGAGEMENT OPTIONS
Choose the right engagement for your stage.
Each engagement builds on the last. One entry point funds the whole relationship
01 UNDERSTAND
RAG Pipeline Audit
£2,500–£3,500
5 working days
A focused diagnostic that tells you exactly where your RAG pipeline is failing and why. You leave with a complete scored breakdown, a prioritised fix list, and a golden test set your team owns long after the engagement ends.
- Complete scored breakdown of every failure point
- Clear picture of where your system generates unreliable answers and why
- Prioritised remediation roadmap with effort-vs-impact ratings
Not sure which engagement is right for your situation? Most clients start with the audit. It scopes the problem clearly and funds the next step. Book a 30-minute call and we will tell you exactly where to begin.
02 IMPROVE
Audit + Remediation Sprint
£4,000–£6,000
2-3 weeks
Everything in the audit, plus we work alongside your engineering team to fix the highest-impact issues. You end the sprint with measurably better pipeline performance and evaluation infrastructure that catches regressions before your users do.
- Everything delivered in the RAG Pipeline Audit
- Hands-on fixes to your highest-priority failure modes
- Automated evaluation integrated into your deployment workflow
Not sure which engagement is right for your situation? Most clients start with the audit. It scopes the problem clearly and funds the next step. Book a 30-minute call and we will tell you exactly where to begin.
03 MAINTAIN
Ongoing RAG Monitoring
£1,500–£2,500/mo
Monthly retainer
Your pipeline changes every time your data, models, or prompts change. This retainer catches quality drift and new failure modes before they reach your users so your team ships with confidence, continuously.
- Continuous scoring against your golden test set
- Drift alerts when answer quality drops below agreed thresholds
- Monthly report with trend analysis and recommended actions
Not sure which engagement is right for your situation? Most clients start with the audit. It scopes the problem clearly and funds the next step. Book a 30-minute call and we will tell you exactly where to begin.
Pipeline Diagram / Methodology
Query Processing
Intent classification, routing
Retrieval
Recall 0.68. Precision 0.71
Context Assembly
Intent classification, routing
Retrieval
Hallucination 12.4% · Faithfulness 0.61
Query Processing
Intent classification, routing
Overall Score
3.4/10
- DeepEval
- RAGAS
- G-Eval
RAG PIPELINE EVALUATION
What we bring that tools alone can't
Most evaluation tools score the final answer. We evaluate every stage of your RAG pipeline. That means you get a precise diagnosis of where the failure originates, not just confirmation that something went wrong.
THE PROBLEM
Standard QA misses the failures that matter.
Traditional test automation was built for deterministic software. It has no way to detect the failures that define AI system quality.
01
Hallucination detection
We detect when the model states facts not supported by retrieved context. Scored using faithfulness and factual consistency metrics.
02
Contextual recall
We measure whether the retrieved chunks contain the information needed to answer the query correctly.
03
Faithfulness scoring
We score whether the generated answer is grounded in the retrieved context or introduces unsupported claims.
04
Contextual relevancy
We evaluate whether the retrieved chunks are actually relevant to the query, not just topically adjacent.
05
Prompt robustness
We test pipeline stability across paraphrased, ambiguous, and edge-case inputs to surface brittle prompt logic.
06
Safety and guardrails
We probe for unsafe outputs, jailbreak vulnerabilities, and failure to apply content filters under adversarial conditions.
07
Regression detection
We track performance changes across model versions, prompt updates, and knowledge base changes to catch regressions before they ship.
08
Production drift monitoring
We identify when live system performance diverges from evaluation baselines as data, models, or user behaviour changes over time.
LLM & RAG TESTING
Standard QA misses the failures that matter.
Each tier builds on the last. One entry point funds the whole relationship.
Each tier builds on the last. One entry point funds the whole relationship.
01
Hallucination passes as correct
Your pipeline returns a confident, well-formatted answer. Standard tests pass. The answer is fabricated.
02
Retrieval fails silently
The wrong chunks are pulled. The answer sounds plausible but is grounded in the wrong context or none at all
03
Answers drift as context grows
Early in a session, responses are accurate. Later, context window pressure degrades them. No standard test catches this
04
Hallucination passes as correct
Your pipeline returns a confident, well-formatted answer. Standard tests pass. The answer is fabricated.
LLM & RAG TESTING
Standard QA misses the failures that matter.
Each tier builds on the last. One entry point funds the whole relationship.
Each tier builds on the last. One entry point funds the whole relationship.
AI Test Group brought real structure and clarity to our AI testing process. Their team quickly identified issues in our LLM workflows, gave us a clear view of where failures were happening, and delivered practical recommendations we could act on immediately. The engagement was thorough, professional, and genuinely valuable for improving reliability.
Adeel Aslam
CTO, BrainX
Standard QA misses the failures that matter.
THE PROBLEM
Traditional test automation was built for deterministic software. It has no way to detect the failures that define AI system quality.
01
Hallucination passes as correct
Your pipeline returns a confident, well-formatted answer. Standard tests pass. The answer is fabricated. Without LLM-specific evaluation, there is no signal. Just a user who has already acted on it.
02
Retrieval fails silently
The wrong document chunks are returned. The model answers anyway, grounded in irrelevant or outdated context. Functional tests see a response. They don't see what fed it.
03
Prompt injection goes undetected
undetectedA user — or embedded content — manipulates your model into ignoring its instructions. There's no error. No alert. Standard test suites aren't looking for it.
04
Context drift degrades accuracy
Early responses are accurate. As the conversation grows, context window pressure causes the model to drop or distort earlier information. No standard test tracks this across turns.
05
Toxic or off-policy responses slip through
Your model produces content that violates your own guardrails — biased outputs, unsafe language, out-of-scope advice. It only surfaces when a user screenshots it.
Questions worth asking before you engage us
What is AI Test Group?
A specialist provider of AI testing services for companies building with LLMs and RAG systems. We offer two things — expert evaluation of your AI layer, and a fully managed QA service for the software those systems run on.
Who do you typically work with?
CTOs, engineering leaders, and senior AI teams responsible for products that are live or close to production. The best fit is a company where failures in reliability, accuracy, or trust have become commercially important.
How is this different from a standard QA agency?
Standard QA agencies test predictable software behaviour. We also test AI-specific failure modes — hallucinations, weak retrieval, prompt sensitivity, unsafe outputs, and inconsistent responses. Those require specialist frameworks that general QA practice was not built to catch.
Where do I start?
Book a 30-minute scoping call. We look at your current system, identify the highest-risk problems, and tell you whether you need LLM testing, RAG evaluation, managed QA, or something else entirely. No obligation, no sales pitch.
Ready to validate your LLM & RAG systems with confidence?
Get comprehensive testing that ensures your Al systems are safe, reliable, and production-ready.