Eval Suite Design
Golden test sets, grading rubrics, and LLM-as-judge scoring calibrated to your specific task.
AI Model Testing & QA
AI Model Testing & QA is the discipline of verifying an LLM or ML system behaves correctly, safely, and consistently before and after it reaches production, a distinct practice from building the AI feature itself. We run automated evals against labeled test sets, hallucination and factuality testing, adversarial red-teaming, and guardrail validation, then wire the same checks into CI and production monitoring so a model or prompt change can't silently degrade quality after launch.
What AI Model Testing & QA Covers
1
Automated Evaluation Suites
- Golden test sets with labeled expected outputs, or graded rubrics for open-ended tasks, that run automatically on every prompt, model, or fine-tune change
- LLM-as-judge scoring for outputs that don't have a single correct answer, calibrated against human-labeled examples so the judge itself is validated
- Regression testing so a prompt tweak that improves one case doesn't silently break ten others
- Tooling such as promptfoo, DeepEval, or Ragas for RAG-specific retrieval and faithfulness metrics, or a custom harness in pytest when off-the-shelf tools don't fit the task
2
Hallucination & Factuality Testing
- Faithfulness testing for RAG systems: checking that generated claims are actually supported by the retrieved source documents, not just plausible-sounding
- Closed-book factuality probes that test whether the model fabricates specifics, such as dates, numbers, citations, or API parameters, it wasn't given
- Consistency testing: asking semantically equivalent questions multiple ways and checking for contradictory answers
- Confidence calibration checks, since a model that's wrong and confident is more dangerous in production than one that's wrong and hedges
3
Adversarial Red-Teaming
- Prompt injection testing: attempting to override system instructions via user input, embedded documents, or tool outputs the model reads
- Jailbreak testing against known attack patterns and role-play or obfuscation techniques, using frameworks like PyRIT or Garak alongside manual adversarial review
- Data exfiltration testing: checking whether the model can be induced to leak system prompts, other users' context, or connected-tool credentials
- Abuse-case testing specific to your product, such as whether the model can be induced to give financial, medical, or legal advice it isn't scoped to give
4
Guardrail Validation
- Testing input filters (topic restrictions, PII detection, injection detection) against both obvious and obfuscated attack attempts
- Testing output filters (schema validation, content policy checks, PII redaction) for both false negatives and false positives
- Verifying escalation and human-in-the-loop paths actually trigger at the confidence thresholds they're configured for, not just that the threshold exists in code
- Failure-mode testing: confirming the system degrades safely, with a clear error, a safe default, or a human handoff, rather than failing open when a guardrail component itself errors
5
Production Monitoring & Drift Detection
- Ongoing sampling and scoring of live production outputs against the same eval rubrics used pre-launch, since offline eval performance and production performance diverge over time
- Drift alerts when output quality, latency, or cost per request moves outside expected bounds, often triggered by an upstream model version change you didn't request
- Structured logging of flagged, escalated, and overridden outputs so you can see what guardrails are actually catching, not just that they exist
- Feedback loops that route production failures back into the eval suite, so the test set grows from real incidents instead of staying static
AI Model Testing & QA Services
Evaluation, safety testing, and monitoring for AI systems already in production or about to ship
Hallucination & Factuality Testing
Faithfulness checks against source documents and closed-book fabrication probes.
Adversarial Red-Teaming
Prompt injection, jailbreak, and data exfiltration testing using established attack frameworks.
Guardrail Validation
Testing input and output filters for both false negatives and false positives, not just presence.
Production Monitoring
Live output sampling, drift detection, and alerting wired into your observability stack.
Regression Testing
CI-integrated checks that catch quality drops from prompt, model, or fine-tune changes.
Benchmark & Model Comparison
Scoring multiple models or prompt versions against the same eval set for an apples-to-apples decision.
Audit-Ready Documentation
Written eval methodology and results suitable for compliance or stakeholder review.
Why Teams Choose SpeedMVPs for AI QA
Testing built to find where a system breaks, not just confirm where it works
We Test the Failure Cases, Not Just the Demo
A model that handles the happy path well can still fail on ambiguous input, adversarial prompts, or edge-case data. Our eval sets are built to find where it breaks, not to confirm where it works.
Red-Teaming Before It's Someone Else's Incident
We run prompt injection, jailbreak, and data exfiltration testing before launch, using the same attack patterns a motivated user would try, so the first adversarial prompt your system sees isn't in production.
Guardrails Are Tested, Not Assumed
A guardrail that exists in code but has never been tested against an obfuscated attack isn't a guardrail, it's a hope. We validate both what gets blocked and what gets wrongly blocked.
Monitoring Doesn't Stop at Launch
Model providers ship silent version updates and your traffic patterns shift over time. We wire the same eval rubrics into production monitoring so drift gets caught in a dashboard, not a customer complaint.
Independent of Who Built the Model or the Feature
AI QA works best as a separate check on the system, whether we built the underlying feature or your own team did. We evaluate against your requirements, not the assumptions baked into the original build.
Why Teams Choose SpeedMVPs for AI QA
Testing built to find where a system breaks, not just confirm where it works
We Test the Failure Cases, Not Just the Demo
A model that handles the happy path well can still fail on ambiguous input, adversarial prompts, or edge-case data. Our eval sets are built to find where it breaks, not to confirm where it works.
Red-Teaming Before It's Someone Else's Incident
We run prompt injection, jailbreak, and data exfiltration testing before launch, using the same attack patterns a motivated user would try, so the first adversarial prompt your system sees isn't in production.
Guardrails Are Tested, Not Assumed
A guardrail that exists in code but has never been tested against an obfuscated attack isn't a guardrail, it's a hope. We validate both what gets blocked and what gets wrongly blocked.
Monitoring Doesn't Stop at Launch
Model providers ship silent version updates and your traffic patterns shift over time. We wire the same eval rubrics into production monitoring so drift gets caught in a dashboard, not a customer complaint.
Independent of Who Built the Model or the Feature
AI QA works best as a separate check on the system, whether we built the underlying feature or your own team did. We evaluate against your requirements, not the assumptions baked into the original build.
AI Model Testing & QA FAQ
Regular software QA tests deterministic logic: given input X, output should always be Y. AI systems built on LLMs are probabilistic, so the same input can produce different outputs, and 'correct' is often a graded judgment rather than a pass/fail check. AI model testing adds methods regular QA doesn't need: LLM-as-judge scoring for open-ended outputs, hallucination and faithfulness testing, adversarial red-teaming for prompt injection and jailbreaks, and drift monitoring, because a model can change behavior in production even when your code hasn't changed.
We define it against a concrete reference wherever possible. For RAG systems, faithfulness testing checks whether each generated claim is actually supported by the retrieved source passages, which is a checkable fact, not a subjective judgment. For closed-book tasks without a retrieval source, we build a labeled test set of prompts with known correct answers and measure the rate of fabricated specifics like dates, numbers, or citations. Where outputs are genuinely open-ended, we use LLM-as-judge scoring calibrated against human-labeled examples, and we report the judge's own agreement rate with humans so you know how much to trust the score.
Red-teaming is adversarial testing: deliberately trying to make the system fail, leak data, or produce content outside its intended scope, the way a motivated bad-faith user would. You need it if your AI system is user-facing, has tool access, or handles anything sensitive, because prompt injection and jailbreak techniques are well-documented and will be tried against a public system eventually, whether or not you tested for them first. We run known attack patterns using frameworks like PyRIT and Garak alongside manual adversarial review, and we report severity so you can prioritize fixes.
We regularly test systems we didn't build. AI QA works as an independent check specifically because it isn't influenced by the assumptions the original build made. We'll need API or sandbox access to the system, your intended use-case scope (what it should and shouldn't do), and ideally logs or examples of real production traffic to build a representative test set.
Continuously for the checks that can run automatically, such as regression evals on every deploy and drift monitoring on live traffic, and periodically for checks that require deliberate human effort, such as red-teaming and guardrail review, typically every quarter or after any material change: a new model version from your provider, a prompt rewrite, a new tool the agent can call, or a new class of user traffic. Providers ship model updates on their own schedule, and a model that passed your evals in January can behave differently in July without a single change on your side.
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Personal fitness companion with AI-driven workout plans and nutrition tracking for optimal health.
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