Published: 2026-04-29 · License: CC BY 4.0
Domain: PSF-2 — Output Validation
Output Validation
What a model produces is only half the story. What your system does with that output — before it reaches a user, triggers an action, or feeds a downstream process — is where PSF-2 begins. Output validation is the last line of defence between an AI system and the world it acts on.
Why Output Validation Is Non-Negotiable
Production AI systems fail in predictable ways at the output boundary. A model produces a plausible-sounding but incorrect answer. A structured output field contains the right data type but an out-of-range value. A content filter misses a policy violation. A downstream system parses the output and propagates a hallucinated fact into a database. Without output validation, the model is both the generator and the final authority on what is true and permissible — which is an untenable position for any production system.
Layers of Output Validation
Every AI output should conform to a declared schema. If the output is structured (JSON, XML, a specific format), validate the schema strictly before passing it downstream. Reject non-conforming outputs — do not attempt to parse them.
For numeric outputs, check that values fall within expected ranges. A sentiment score outside [0,1] is a model failure, not user error. A date field containing a future date when historical dates are expected should trigger a review, not pass silently.
Apply content policy enforcement after inference. Even if input governance filtered the prompt, models can produce policy-violating content from benign inputs. Post-inference filtering is a required backstop, not an alternative to input governance.
For RAG or grounded systems, verify that outputs are supported by the retrieved context. Citation grounding checks — does the claim appear in the source material? — are the primary hallucination mitigation available at inference time.
When a model expresses low confidence (or when an external confidence scoring system is applied), gate the output. Low-confidence outputs should not flow into high-stakes downstream processes — they should trigger human review or a fallback path.
Define explicit behaviour for every output validation failure: what does the system do when the output fails schema validation? When confidence is too low? When content policy is violated? Undefined failure modes produce unpredictable production behaviour.
Structured Output Contracts
The most robust output validation architecture uses structured output contracts — a formal declaration of what a model is expected to produce. Modern LLM APIs support JSON schema-constrained generation, which constrains the model at generation time rather than validating after the fact. This reduces the surface area for malformed outputs but does not eliminate the need for semantic validation. A schema-conforming output can still be factually wrong, out of range, or policy-violating. Structural conformance and semantic correctness are different properties.
PSF-2 Compliance Checklist
The Cascade Risk
In multi-agent or pipeline architectures, an unvalidated output from one stage becomes the input to the next. A hallucinated entity name passes into a database lookup. A schema-invalid field causes a downstream parser to misread adjacent valid fields. A confidence-gated output that bypasses review triggers an irreversible action. The cascade risk of unvalidated AI outputs grows with every hop in the pipeline. PSF-2 controls must exist at every integration boundary — not only at the final output to the user.
AIDA Exam Tips for PSF-2
- PSF-2 is about what happens AFTER the model call. If the fix involves the model's input or the system prompt, it is PSF-1. If it involves what the model produced, it is PSF-2.
- Schema validation and content filtering are the two most commonly tested PSF-2 controls. Know the difference: schema is structural, content policy is semantic.
- Confidence gating questions often describe a scenario where a low-confidence output caused harm because it was not routed to review. The answer is always a PSF-2 confidence threshold + human review path.
- In pipeline/multi-agent questions, the PSF-2 answer is to validate outputs at every inter-stage boundary, not only at the final output.
- Hallucination questions that involve RAG systems: the PSF-2 control is citation grounding — checking that output claims are supported by retrieved documents.