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Accuracy & Relevance
Factual correctness, completeness, and response relevance.
LLM Testing Services
Your LLM app can pass unit tests and still give customers the wrong answer. PrimeQA tests LLM applications for accuracy, hallucinations, security, safety, reliability, and performance before failures reach production.
The Problems We Fix
The Problems We Fix
If you are shipping an LLM application, these are the failures that reach users. Each one has a test answer.
"It gives confident answers that are wrong."
There is no check tying each claim back to a source. We test grounding at the claim level, not the response level.
"It answered differently the second time."
Output is sampled, not computed. We run the same and paraphrased prompts repeatedly and measure variance against an agreed threshold.
"The vendor updated the model and something broke."
Hosted models change without your code changing. Versioned baseline suites make that shift visible before release.
"One prompt tweak changed behavior everywhere."
Prompts are untested configuration running in production. We treat every prompt edit as a regression trigger.
"It pulled the wrong document."
Stale index, weak chunking, or poor retrieval ranking. We test retrieval separately from generation, so the fix is obvious.
What We Test
What We Test
We test every layer of your LLM application, from response quality and hallucinations to security, performance, and reliability, to uncover failures before they reach users.
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Hallucination Testing
Detect unsupported or fabricated claims and trace responses to source data and context.
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Prompt Testing
Test ambiguity, paraphrasing, typos, conflicting instructions, adversarial prompts, and out-of-scope requests.
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Bias & Fairness
Compare responses across demographic and contextual variations for unfair differences.
Reliability & Consistency
Test repeated and equivalent prompts for unacceptable output variations.
Guardrail Testing
Verify that safety controls block harmful requests without unnecessarily blocking legitimate ones.
Make Your LLM Application More Reliable
PrimeQA tests your application for accuracy, reliability, safety, security, and performance, helping you catch issues before they reach users.
Our LLM Testing Methodology
Our LLM Testing Methodology
We follow a structured LLM testing approach that combines expert QA, automated evaluation, and continuous monitoring to identify risks at every stage.
Assess
Map your models, prompts, RAG pipeline, tools, guardrails, and dependencies.
Define
Set evaluation criteria, metrics, and pass/fail thresholds based on your use case.
Build
Create domain-specific datasets, prompts, adversarial tests, and baseline test suites.
Execute
Test functionality, accuracy, hallucinations, safety, security, performance, and reliability.
Automate
Integrate repeatable LLM tests into CI/CD for regression detection and release validation.
Monitor
Continuously evaluate model behavior, detect drift, and report issues between releases.
Services
Our LLM Testing Services
Our LLM testing services cover the critical areas that affect the quality and reliability of AI applications.
LLM Accuracy Testing
Correctness measured against curated ground truth datasets.
LLM Evaluation and Validation
Metric definition, scoring frameworks, and quality scorecards.
LLM Hallucination Testing
Claim-level grounding checks and fabrication detection.
Prompt Testing
Robustness across paraphrase, ambiguity, and adversarial phrasing
LLM Guardrail Testing
Enforcement and over-blocking verification.
AI Red Teaming
Structured adversarial testing of the full application.
How We Measure
How We Measure LLM Quality
There is no single quality score for an LLM application. We select metrics by use case and weight them by what failure costs.
Accuracy
Relevance
Faithfulness
Groundedness
Completeness
Coherence
Consistency
Hallucination rate
Toxicity
Bias indicators
Latency
Token consumption
The Testing Framework
Testing only the final answer tells you something broke, not where. We evaluate at every stage.
Standard LLM application:
Input → Prompt → Model → Tools and Retrieval → Output → Evaluation → Decision
RAG application:
Query → Retrieval → Context → Prompt → LLM → Response → Evaluation
Agentic system:
Goal → Planning → Tool Selection → Execution → Observation → Next Action → Response
Tools and Technologies We use
Tools and Technologies We use
Tooling follows your architecture, not a fixed stack.
Models
OpenAI Anthropic GeminiGroq
Open models
Hugging Face LangChainLangGraphLlamaIndex
For evaluation
DeepEval Ragas PromptfooOpenAI EvalsLangSmith
Test automation
PythonPytest
Download Free Software Testing Report Templates
Access a complete collection of software testing reports and templates for every QA phase.
- 20+ ready-to-use QA report templates
- Covers functional, automation, API, performance, security & mobile testing
- Fully customizable for your projects
Case Studies
Inside Our AI Testing Approach: A Real-World Case Studies
AI-specific testing to validate an AI-powered Shopify application, identify critical defects, and improve confidence in its production readiness.
Ed-Tech
Semantic Testing of GroBro’s AI Chatbot
25
Prompts Tested
60%
Semantic Similarity Threshold
100%
Automated Response Validation
2-Layer
UI + Semantic Testing
E-COMMERCE
How AI Chatbots Navigate Localization and Regulatory Hurdles
40%
Improved Stability
35%
Fewer Critical Defects
100%
Milestone Adherence
2x
Faster Issue Resolution
Saas
Validating AI-Driven Business Intelligence Analytics Platform for a Shopify Merchants
17
AI Problem Statements
200+
AI Test Cases
150+
APIs Tested
1+
Year QA Engagement
What You Receive
Get a complete LLM testing package tailored to your architecture, use case, and risk profile, including:
LLM test strategy and detailed test cases
Prompt, adversarial, and injection test suites
Curated test datasets and evaluation criteria
Automated test scripts and evaluation frameworks
Hallucination, security, and safety findings
Regression and performance reports
Detailed defect reports with traceable evidence
Per-release AI quality scorecards
Prioritized recommendations for prompts, RAG, and guardrails
CI/CD integration guidance for continuous LLM evaluation
100+ LLM Test Scenarios
50+ AI Quality Checks
20+ Security & Safety Tests
100% Traceable Test Evidence
Why PrimeQA
Why PrimeQA
PrimeQA brings independent QA expertise to LLM testing, so your AI application is evaluated objectively, not by the team that built it.
Independent QA Expertise
Unbiased testing backed by proven test strategy, coverage, and defect management.
AI Testing Experience
Experience testing LLM applications, RAG pipelines, model migrations, and AI-generated recommendations.
Automation-First Approach
Automated evaluation frameworks and CI/CD quality gates for repeatable testing.
Engineering Depth
We analyze code, prompts, APIs, and integrations to provide actionable root-cause insights.
Experienced QA Team
QA engineers with 5+ years of average experience across functional, automation, API, performance, security, and AI testing.
AI
Let's Work Together
Choose the Engagement Model That Fits Your Project
Whether you need a focused LLM assessment or ongoing AI quality engineering, our flexible engagement models adapt to your application, testing goals, and release cycle.
Let's Talk
Ready to Write Your Own Success Story?
Partner with PrimeQA to build a scalable QA strategy tailored to your industry. Tell us about your project and we'll get back within one business day.
- Free 30-minute discovery call
- Custom testing strategy included
- No commitment required