AI STRATEGY
Build Guardrails and Escalation Paths
Keep Unsafe Content Out
Prompt and output filtering is your first line of defense in ensuring safe and appropriate AI interactions. By screening both inputs and outputs, you minimize the risk of harm and build user trust.
Why it's Important
Prevents harmful, biased, or offensive content from reaching users
Supports regulatory compliance and safety standards
Builds brand reputation and user confidence
Reduces risk of platform misuse
Helps define system boundaries clearly
How to Implement
Create a keyword list for input and output filters (e.g., hate speech, violence)
Use regex or classification models to detect risky inputs
Filter or rephrase outputs using pre/post-processing
Categorize violations by severity (e.g., soft flag vs. block)
Include real-world context in your risk list
Maintain and version your filters as language evolves
Log violations for review and tuning
Available Workshops
Offensive Prompt Mapping
Risk Phrase Brainstorm
Pre/Post Filtering Simulation
Regulatory Trigger Term Review
Content Escalation Roleplay
Filter Sensitivity Testing
Deliverables
Prompt filtering ruleset
Output sanitization logic
Risk category definitions
Filter test suite with examples
Weekly violation report
How to Measure
Number of blocked or flagged prompts
False positive/negative rates
Time-to-detect unsafe content
Frequency of filter updates
Severity distribution of violations
% of filtered outputs rerouted to fallback responses
Pro Tips
Add comments to explain each filter rule
Use 3rd party solutions if appropriate
Monitor evolving slang or adversarial prompts
Use fallback messages that preserve trust
Track filter impact on user satisfaction
Pair filters with escalation for gray areas
Get It Right
Align filter lists with user personas and industry context
Make filters transparent and explainable to internal teams
Continuously test and tune thresholds
Combine lexical and ML-based filters for coverage
Balance safety with UX clarity
Don't Make These Mistakes
Using only static keyword lists
Over-filtering and suppressing valid content
Ignoring tone or context in filters
Failing to update filters as slang or risks change
Treating filtering as "set it and forget it"