A demonstration proves possibility. A release decision needs representative tasks, known failure cases, quality thresholds, and a process to inspect what went wrong.
Separate demonstration from evidence
A successful demo usually uses clean examples and a helpful operator. Production users bring incomplete requests, conflicting records, misleading instructions, and unfamiliar terminology. The evaluation plan must deliberately include these conditions. Otherwise, teams optimize for a smooth presentation rather than dependable behavior.
Build a reviewed evaluation set
Use real work samples with sensitive information removed or controlled. Include normal cases, difficult but valid cases, ambiguous requests, insufficient-evidence cases, policy exceptions, and adversarial inputs. Have subject-matter experts define what makes an answer acceptable, incomplete, unsafe, or wrong before scoring begins.
| Measure | What it reveals | Release question |
|---|---|---|
| Task success | Whether the output solves the intended task | Is quality acceptable for the scoped workflow? |
| Groundedness | Whether claims trace to approved evidence | Can users inspect and trust the basis? |
| Safety compliance | Whether forbidden actions or content are avoided | Does the workflow stop when it should? |
| Escalation quality | Whether uncertain cases route correctly | Do reviewers get enough context to decide? |
| Operational reliability | Latency, failure handling, and recoverability | Can the process operate at expected volume? |
Evaluate the whole workflow
Model quality is only one variable. Test retrieval, permissions, tool execution, approval handoffs, audit logging, and user interface clarity. An answer can be accurate but still fail if it exposes the wrong source, cannot be acted on, or leaves no record of a consequential change.
Define release gates in advance
Set criteria before seeing results: for example, all high-risk cases must escalate; citations must be available where evidence is used; reviewers must be able to override; and failure behavior must preserve the system of record. The exact thresholds depend on the workflow, but the principle does not: the team should know what would prevent release.
Evaluation loop
Representative cases → independent review → error taxonomy → corrective change → regression evaluation → release decision. Keep the same difficult cases in the regression set so improvements do not reintroduce known failures.
Sources and further reading
- NIST AI Risk Management Framework — a practical risk-management vocabulary for AI systems.
- OWASP Top 10 for LLM Applications — implementation risks to test and control.
- NIST AI RMF Playbook — actions for governing, mapping, measuring, and managing AI risk.
Turn the framework into a working plan
SoloSoft can help define the workflow, system boundaries, success measures, and delivery sequence before implementation begins.