Can we trust this AI in production?
Frameworks, metrics, and reliability patterns for testing LLM applications, agents, and RAG systems — written for engineering teams shipping AI to real users.
Why AI testing is its own discipline
Classical QA assumes deterministic systems with binary outcomes. Modern AI is probabilistic, open-ended, and brittle in surprising ways: a 0.1 temperature change, a re-indexed vector store, or a new system prompt can silently regress production behavior overnight.
The AI Testing Hub collects the methodologies AIQASolver uses with enterprise teams — golden datasets, LLM-as-judge evals, regression harnesses, red-team scenarios, and continuous monitoring — so your team can answer one question with evidence: can we trust this AI in production?
Topic cluster
Deep dives, frameworks, and validation patterns across this domain.
LLM Testing
Accuracy, factuality, format, safety, latency, and cost across model versions.
ExplorePrompt Testing
Regression-test prompts like code: versioned, scored, and gated in CI.
ExploreRegression Testing
Catch silent quality drops when prompts, models, or retrievers change.
Evaluation Frameworks
DeepEval, RAGAS, Promptfoo, OpenAI Evals — what to use when.
ExploreAI Quality Assurance
Stand up an AI QA function: roles, rituals, eval sets, and dashboards.
AI Validation
Pre-launch validation for accuracy, fairness, safety, and resilience.
AI Risk Assessment
Identify and rank model, data, and behavior risks before they ship.
AI Monitoring
Drift, hallucination, cost, and latency telemetry in production.
Frequently asked
What is AI testing?
AI testing validates that machine-learning and LLM-driven systems behave correctly, safely, and consistently under realistic production conditions — covering accuracy, robustness, bias, safety, latency, and cost.
How is AI testing different from traditional QA?
Outputs are probabilistic, inputs are open-ended, and failures are statistical rather than binary. AI QA combines example-based assertions, LLM-as-judge evaluations, and continuous monitoring instead of pure pass/fail tests.
What should go into an AI test plan?
A representative golden dataset, task-specific metrics, regression suites, adversarial / red-team scenarios, latency and cost budgets, hallucination checks, and a production monitoring loop.
When should I start AI testing?
Before your first prompt change ships. Establish a baseline eval set on day one so every prompt, model, or retrieval change can be measured instead of guessed.
Related tools
Can we trust this AI in production?
Get an independent assessment from senior AI quality engineers.