AI Testing & QA Automation Services
Test AI systems before they reach customers: prompt accuracy, RAG grounding, agent workflows, tool use, prompt injection, PII/data-leakage, cost, latency and an automated regression suite.
Last updated: 2026-07-16. Provider: SnapSiteBuild AI Centre of Excellence — founder Krish Chimakurthy.
AI testing is the discipline of proving that an LLM, AI agent or RAG system behaves correctly, safely and predictably before it reaches real users — and again on every prompt, model or data change after launch. Ordinary unit tests are not enough, because generative AI is non-deterministic and can be confidently, plausibly wrong. SnapSiteBuild runs a structured AI QA programme: a written test plan, measurable evaluation metrics, adversarial security checks and an automated regression suite that gates production.
TL;DR. We test eight things — prompt accuracy, RAG grounding, hallucination, agent and tool-use, prompt injection, PII and data leakage, latency and cost — then turn the results into a pass/fail production-readiness gate and a re-runnable regression suite you keep.
- What: AI testing services covering LLM testing, RAG testing, AI agent testing, prompt testing, prompt injection testing and AI model evaluation.
- How: test plan → labelled eval datasets → metrics and scoring → defect triage → readiness gate → automated regression suite.
- Why a separate QA practice: AI fails differently from normal software — it is plausibly wrong, can leak data, and can be manipulated by injected instructions.
- Output: a scored readiness report, an eval dataset and a CI-ready test suite that re-runs on every change.
- Next step: a short scoping call — tell us about your AI system.
What we test — and what each test catches
Most AI incidents are not crashes; they are answers that look fine but are wrong, unsafe or off-policy. Our AI QA automation maps each test type to the specific failure it catches and a representative signal we score it against. We tune the exact metrics and pass thresholds to your use case, data sensitivity and risk tolerance.
| AI test type | What it catches | Signal we score |
|---|
| Prompt accuracy & instruction-following | Wrong, off-format or off-policy answers; ignored constraints | Exact-match / rubric score against a gold answer set |
| RAG grounding & retrieval | Ungrounded or fabricated answers; the right document never retrieved | Retrieval recall plus answer groundedness and citation faithfulness |
| Hallucination | Plausible but false claims, invented facts, fake citations | Claim-level hallucination rate against reference sources |
| Agent & tool-use | Wrong tool, bad arguments, broken multi-step or looping workflows | Tool-call correctness and end-to-end task success rate |
| Prompt injection & jailbreak | Manipulated instructions, policy bypass, data exfiltration via inputs | Attack success rate across an adversarial prompt suite |
| PII & data leakage | Sensitive data surfacing in outputs, logs or tool calls | PII detection and redaction pass rate |
| Latency & cost | Responses too slow for the workflow or too expensive to run at scale | p50/p95 latency and tokens / cost per resolved task |
| Regression | Quality silently dropping after a prompt, model or data change | Pass-rate delta versus the approved baseline |
How we test: from test plan to production-readiness gate
AI testing is a delivery practice, not a one-off prompt review. We run it as a repeatable pipeline so results are comparable release over release, and so the same suite that signs off launch keeps guarding the system afterwards.
- Test plan and risk model. Define the use case, the user inputs that matter, failure modes that would hurt, and the metrics and thresholds that constitute "good enough" for go-live.
- Build the eval dataset. Curate representative and edge-case inputs with expected outputs or grading rubrics — including hard, adversarial and out-of-scope cases, not just the happy path.
- Score with evaluation metrics. Run automated evals using exact-match and semantic similarity, retrieval and groundedness checks, an LLM-as-judge with human spot-checks, and security and PII classifiers.
- Triage defects. Log each failure with severity and a likely root cause — prompt, retrieval, model choice, tool wiring or data — so fixes target the real source, then re-test.
- Apply the readiness gate. Compare scores to the agreed thresholds; release only when accuracy, grounding, injection resistance, PII safety, latency and cost all pass.
- Automate the regression suite. Wire the evals into CI so every prompt edit, model swap, RAG re-index or dependency bump re-runs the gate and blocks silent quality loss.
Evaluation metrics we commonly use
The right metric depends on the task, but these are the workhorses we configure and threshold per project.
- Correctness: exact-match, semantic similarity and rubric-graded scores against a labelled set.
- Grounding: retrieval recall and precision, answer groundedness and citation faithfulness for retrieval-augmented systems.
- Safety: prompt-injection attack success rate, jailbreak resistance, toxicity and refusal correctness.
- Privacy: PII detection and redaction pass rate across outputs, logs and tool calls.
- Performance: p50/p95 latency, throughput and cost per resolved task or conversation.
Why AI needs its own QA discipline
Traditional software is deterministic: the same input gives the same output, and a passing unit test stays passing. Generative AI breaks both assumptions, so it needs behaviour-level evaluation rather than only code-level assertions.
- It is plausibly wrong. A hallucinated answer reads as confidently as a correct one, so failures slip past human reviewers and need measured hallucination and grounding checks.
- It is non-deterministic. Outputs vary run to run, so QA evaluates distributions and pass rates over a dataset, not a single golden string.
- It can be attacked through content. Prompt injection turns ordinary user or document text into instructions, so security testing belongs in the QA loop.
- It drifts. A model upgrade or re-indexed knowledge base can quietly change behaviour, which is exactly what the regression suite is there to catch.
This is most acute for AI agents that call tools and take actions and for RAG and knowledge-base systems, where a single wrong retrieval or tool argument can cascade through a whole workflow. Where models run on a private local LLM setup or behind an enterprise AI gateway, we also test that prompt-firewall, redaction and audit controls actually fire under adversarial input.
Who runs your AI testing — and why trust it
SnapSiteBuild is an AI Centre of Excellence founded by Krish Chimakurthy, who brings close to two decades of IT delivery, software testing and project management alongside hands-on AI engineering. AI QA is where that testing heritage pays off: disciplined test plans, traceable defects and clear release gates, applied to a new class of non-deterministic systems.
The evidence is in the deliverables, not in slogans — you keep everything we build, and every result is reproducible by re-running the suite. We do not publish invented client logos or accuracy figures; we report your system's measured scores against thresholds you approve. A typical engagement leaves you with:
- A written AI test plan and risk model for your use case.
- A labelled evaluation dataset, including adversarial and edge cases.
- A scored production-readiness report with pass/fail against each gate.
- A triaged defect log with root causes and recommended fixes.
- An automated regression suite wired into CI for ongoing release safety.
Testing slots into delivery as a quality gate within AI project management, and ships as standard with new AI apps and MVPs so nothing reaches users untested.
Frequently asked questions
- How do you test an LLM or AI agent?
- With a written test plan and evaluation metrics run over a labelled dataset — covering prompt accuracy, RAG grounding, hallucination rate, tool-use correctness, prompt-injection resistance, PII leakage, latency and cost — then a repeatable regression suite that gates production. For agents we also assert end-to-end task success and correct tool calls across multi-step flows, not just single answers.
- Why does AI need separate testing from normal software?
- Because AI fails differently. It can be confidently and plausibly wrong, leak sensitive data, or be manipulated by prompt injection — and being non-deterministic, it can pass once and fail the next run. That requires behaviour-level evaluation over many cases, security testing and drift monitoring, on top of normal unit and integration tests.
- What is a production-readiness gate?
- It is a pass/fail checklist of evaluation thresholds — for accuracy, grounding, injection resistance, PII safety, latency and cost — that a release must clear before go-live. The same gate runs in CI on every change, so a prompt edit, model swap or knowledge-base update cannot ship if it drops below the approved bar.
- How do you test for prompt injection?
- We run an adversarial suite of direct and indirect injections, jailbreaks and tool-abuse attempts — including hostile instructions hidden inside retrieved documents — and measure the attack success rate. Failures are closed with input and output guardrails, a prompt firewall, least-privilege tool permissions and human approval for sensitive actions, then the suite re-runs to confirm the fix.
- How do you measure and reduce hallucinations?
- We check claims in the answer against grounded source material and reference data, using automated comparison plus an LLM-as-judge with human spot-checks, to produce a hallucination rate. We reduce it by grounding answers in retrieval, requiring citations, constraining the prompt and scope, and choosing models that perform better on your evals — then we re-measure.
- How do you test RAG grounding?
- We separate two failure points: retrieval quality (did the system fetch the right chunks?) and generation faithfulness (did the answer stay within them?). Using a labelled question/answer set, we score retrieval recall and precision alongside answer groundedness and citation faithfulness, so you can see whether a wrong answer came from bad retrieval or from the model drifting off its sources.
Ready to ship AI you can trust?
If you are about to launch an LLM feature, agent or RAG assistant — or one is already live without a test suite behind it — we will scope an AI testing plan and a production-readiness gate around it. Contact SnapSiteBuild to get started.
Contact SnapSiteBuild: Customersupport@snapsitebuild.com