- Validation vs Moderation (Important Distinction)
- Field-Level Validation (First Line of Defense)
- Cross-Field and Logical Validation
- AI-Assisted Interpretation (After Validation)
- Content Moderation
- Moderation Strategies
- Blocking vs Redirecting Invalid Input
- Preventing Prompt Injection
- Validation and Moderation in Workflows
- Logging and Visibility
- Performance and Cost Considerations
- What Validation & Moderation Do Not Do
- Common Mistakes
- Best Practices
- Summary
Form Input Validation & Moderation define how AI Forms ensure that submitted data is correct, safe, usable, and appropriate before it reaches AI reasoning or triggers any downstream action. In Aimogen, validation and moderation are intentional control layers, not afterthoughts and not delegated blindly to AI.
Good validation protects workflows. Good moderation protects users and systems.
Validation vs Moderation (Important Distinction) #
These are two different concerns.
Validation answers:
- Is the input complete?
- Is the format correct?
- Does the value make sense?
- Does it meet required constraints?
Moderation answers:
- Is the input acceptable?
- Is it safe to process?
- Does it contain abuse, spam, or disallowed content?
Validation ensures correctness.
Moderation ensures safety.
Both are needed.
Field-Level Validation (First Line of Defense) #
Field-level validation happens before AI is involved.
Typical validation includes:
- required vs optional fields
- type checks (text, number, email, URL)
- length limits
- numeric ranges
- allowed values
- pattern matching
Invalid input should never be sent to AI. AI is not a validator.
Cross-Field and Logical Validation #
Some validation depends on relationships between fields.
Examples:
- budget must exist if “paid service” is selected
- deadline must be in the future
- company size must match selected plan
- region must match available services
This logic is deterministic and rule-based.
AI should not be used to infer logical correctness.
AI-Assisted Interpretation (After Validation) #
Once data is structurally valid, AI can:
- interpret intent
- summarize free-text input
- classify requests
- detect ambiguity
- normalize phrasing
AI interpretation happens after validation, never before.
Content Moderation #
Moderation protects against:
- spam
- abusive language
- hate speech
- prompt injection attempts
- attempts to manipulate AI behavior
- irrelevant or malicious content
Moderation can be applied to:
- free-text fields
- optional comments
- descriptions
- uploaded text content
Moderation should be explicit and conservative.
Moderation Strategies #
Moderation can be implemented using:
- strict allow/deny rules
- keyword or pattern checks
- AI-based moderation models
- hybrid approaches
AI moderation is useful for nuance, but rule-based checks should exist for hard limits.
Blocking vs Redirecting Invalid Input #
When validation or moderation fails, you can:
- block submission entirely
- request clarification
- ask the user to rephrase
- show a specific error
- terminate the flow
- route to manual review
Failing silently is always a mistake.
Preventing Prompt Injection #
AI Forms are common targets for prompt injection attempts.
Mitigation strategies include:
- never passing raw input directly into system instructions
- separating user input from AI control prompts
- enforcing strict AI instructions
- moderating suspicious patterns
- limiting free-text fields
Structure is your best defense.
Validation and Moderation in Workflows #
In complex setups:
- validation happens at the form level
- moderation happens before AI execution
- OmniBlocks can enforce additional checks
- assistants apply behavioral constraints
- actions only run after all checks pass
Each layer has a role.
Logging and Visibility #
Validation and moderation failures should be:
- logged clearly
- traceable to the rule that failed
- reviewable by administrators
Logs help improve form design and detect abuse patterns.
Performance and Cost Considerations #
Proper validation:
- reduces unnecessary AI calls
- lowers token usage
- improves response speed
Moderation before AI is cheaper than cleaning up after AI mistakes.
What Validation & Moderation Do Not Do #
They do not:
- guarantee perfect data
- replace business logic
- infer missing information
- enforce legal compliance automatically
- protect against all abuse
They reduce risk, not eliminate it.
Common Mistakes #
- relying on AI to validate input
- skipping moderation on free-text fields
- collecting unnecessary user data
- allowing raw input into system prompts
- ignoring validation failures
- treating moderation as optional
Validation and moderation are not UX polish. They are safeguards.
Best Practices #
Validate early, moderate before AI, keep rules explicit, limit free-text input, log failures, and never trust user input — even when it looks harmless. Let AI interpret meaning, not decide whether data is acceptable.
Summary #
Form Input Validation & Moderation in Aimogen ensure that AI Forms receive clean, correct, and safe data before any reasoning or automation occurs. Validation enforces structure and correctness; moderation enforces safety and intent boundaries. Together, they protect workflows from abuse, reduce AI errors, lower costs, and make AI-powered forms reliable instead of fragile.
