Evals measure Quality.
Audit proves Compliance.
They are not different approaches to the same problem. They are solutions to different problems. Conflating them is a regulatory risk.
It Has Always Existed
The path to reliability is not a new concept invented for AI. It is a universal law of industrial process — whether in manufacturing, finance, or healthcare.
The triumvirate remains: Observability (seeing it), Testing (verifying it), and Audit (proving it). Most teams stop at step two.
Observability
“What happened?”
Evaluation
“Did it work correctly?”
Audit
“Can you prove it to regulators?”
What Changed: AI in the Middle
Traditional software is deterministic: same input always equals same output. AI systems are probabilistic: the same input can produce different outputs every time. This breaks traditional testing.
Control Panel
Click multiple times to observe how the same input produces different outputs. This is the core “Probabilistic Problem”.
The Complexity Gradient
Why “Unit Tests” stopped working for AI.
Task Type
Recommended Approach
Hybrid (Rules + weak Eval)Example Prompt
Verification Truth Source
The Sampling Trap
In a call center with 250,000 interactions, a team of 20 nurses can only review about 3-5% of cases.
This “sampling approach” relies on the dangerous assumption that the 3% you see represents the 97% you don't.
LLM-as-Judge promised to fix this by reviewing 100%. And for quality monitoring (trends), it works. But for Compliance, it fails because it cannot guarantee the same result twice.
Input Parameters
Projection Analysis
The Evaluation Methods Landscape
Not all evaluation methods are created equal. Each has distinct trade-offs across coverage, consistency, nuance, cost, and auditability.
Human Expert Review
Human Expert Review
The gold standard. Contextual, credible, but expensive.
Rule-Based
Fast and deterministic. Good for format, terrible for nuance.
Statistical
Pattern detection. "This call was 3x longer than average."
Reference Comparison
Comparing output to a Golden Dataset. Expensive to maintain.
LLM-as-Judge
Scalable judgment. Handles nuance but lacks audit trails.
They Solve Different Problems
Evals and Audits are not competing approaches to quality. They operate on different axes entirely. Conflating them creates a compliance gap that no amount of prompt engineering can close.
Evals
Audit
The Governance Blind Spot
Why guardrails, evals, and human-in-the-loop don't close the compliance gap.
CogniSwitch Audit
Deterministic compliance trails for every AI decision.
Talk to Us
See how deterministic audit infrastructure works for your use case.
Start a conversation