Complete Testing Suite

LLM Testing That Actually Works

Stop hoping your AI works. Start proving it. ArtemisKit is the open-source CLI for testing LLM applications with quality checks, security scans, and stress tests.

Start Testing in 5 Minutes View on GitHub
$ npm install -g @artemiskit/cli
LLM Testing Suite
$ akit run customer-support.yaml
Running 5 test cases...
✓ Password reset response similarity: 0.94
✓ Billing inquiry handling contains: pass
✓ Refund policy explanation llm_grader: 0.91
✓ Product recommendation schema: valid
✓ Escalation trigger not_contains: pass
Results: 5/5 passed
Run ID: llm_abc123 • Duration: 2.3s
The Challenge

The Problem with LLM Testing

Traditional testing doesn't work for LLMs. Same input, different outputs. Assertions fail on valid responses. Manual testing doesn't scale.

Non-Deterministic Outputs

LLMs don't return the same output twice. Traditional assertion-based testing fails on perfectly valid responses.

Security Blind Spots

Prompt injection is OWASP #1 for LLMs. Most teams don't test for it. Your model is one clever prompt away from disaster.

Unknown Performance

You'll discover your latency limits in production. Or you could know them before your users do.

The Solution

Complete LLM Testing in One CLI

ArtemisKit combines quality evaluation, security red-teaming, and performance stress testing. Most teams use 3+ tools. You need one.

12 Evaluators

Quality Evaluation

12 evaluator types including semantic similarity, LLM-as-judge, JSON schema validation, and fuzzy matching.

akit run scenario.yaml
6 Attack Types

Security Red-Teaming

6 mutation types: prompt injection, jailbreaks, role spoofing, instruction flipping, encoding attacks, multi-turn.

akit redteam scenario.yaml
p50/p95/p99

Performance Stress Testing

Measure p50/p95/p99 latency, throughput limits, token costs, and error rates under realistic load conditions.

akit stress scenario.yaml -c 50 -d 60
Quick Start

How LLM Testing Works

Get from zero to tested in three steps. No account required. No vendor lock-in.

1

Define Your Scenario

Write a YAML file describing your test cases, expected behaviors, and evaluation criteria.

scenario.yaml 
name: customer-support-bot
provider:
  type: openai
  model: gpt-4o
cases:
  - prompt: "How do I reset my password?"
    expected:
      contains: "account settings"
2

Run Your Tests

Execute quality checks, security scans, or stress tests with a single command.

Terminal
$ akit run scenario.yaml
$ akit redteam scenario.yaml
$ akit stress scenario.yaml -c 50
3

Review Results

Get detailed reports with pass/fail status, metrics, and actionable insights.

Output
✓ 45/50 tests passed
✓ 0 security vulnerabilities
✓ p99 latency: 1.2s
Run ID: llm_abc123
Use Cases

LLM Testing Use Cases

ArtemisKit is built for teams shipping AI to production.

CI/CD Quality Gates

Block deployments that fail quality, security, or performance thresholds. Integrate with GitHub Actions, GitLab CI, or any CI system.

Learn more

Pre-Deployment Security

Red-team your LLM before attackers do. Test for prompt injection, jailbreaks, and data extraction vulnerabilities.

Learn more

Regression Detection

Catch quality regressions after prompt changes, fine-tuning, or model updates. Compare runs to track improvements.

Learn more

Capacity Planning

Know your latency limits before users find them. Stress test to find throughput limits and estimate costs at scale.

Learn more
FAQ

Frequently Asked Questions

What is LLM testing?

LLM testing is the process of systematically evaluating Large Language Model outputs for quality, accuracy, security vulnerabilities, and performance under load. It ensures your AI application behaves correctly before deployment.

Why is LLM testing different from traditional software testing?

LLM outputs are non-deterministic - the same input can produce different outputs. Traditional assertion-based testing doesn't work. LLM testing requires semantic evaluation, fuzzy matching, and statistical analysis to measure quality.

What does ArtemisKit test for?

ArtemisKit tests three dimensions: Quality (semantic similarity, LLM-as-judge, schema validation), Security (prompt injection, jailbreaks, data extraction), and Performance (latency percentiles, throughput, token costs under load).

Can I integrate LLM testing into CI/CD?

Yes. ArtemisKit is designed for CI/CD integration with proper exit codes, JSON reports, and GitHub Actions support. You can block deployments that fail quality, security, or performance thresholds.

How long does it take to set up LLM testing?

With ArtemisKit, you can run your first test in under 5 minutes. Install with npm, create a scenario YAML file, and run 'akit run scenario.yaml'. No account required.

Related Articles

Tutorial

Getting Started with LLM Testing

A practical guide to testing your first LLM application with ArtemisKit.

 Engineering

Understanding Semantic Similarity

How semantic similarity evaluation works and when to use it for LLM testing.

 Security News

Klarna AI Customer Service Failure

What went wrong and why AI testing matters for production systems.

Stop Hoping. Start Proving.

ArtemisKit is free, open-source, and ready to help you ship AI with confidence. Your first test is 5 minutes away.

Get Started Free Read the Docs