Ensuring AI Reliability in Production
Implementing rigorous evaluation frameworks, unit tests, and guardrails for non-deterministic AI agents.
Why AI Agent Testing & Evaluation Matters
You cannot deploy AI to production without knowing how it behaves. Traditional unit tests fail on non-deterministic LLM outputs, requiring specialized evaluation frameworks.
| Market Signal | Impact Detail |
|---|---|
| Employer Demand | A rapidly growing requirement as companies move AI from prototype to production. |
How We Use It
We build custom 'eval' pipelines that use LLMs to judge the output of other LLMs against a gold-standard dataset, ensuring quality doesn't degrade over time.
Real World Example
We built an evaluation suite for an AI customer service agent that continuously runs 5,000 test conversations nightly, alerting the team to any regressions in tone or accuracy.
The Slickrock Advantage
"We treat AI prompts like code, requiring them to pass strict evaluation test suites before they can be merged into the main branch."
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Frequently Asked Questions
How do you test an LLM if the output changes?
You evaluate based on criteria (e.g., 'Did it mention the refund policy?') rather than exact string matching.