Learn how to implement guardrails, follow guidelines, and mitigate bias
Creating safe, fair, and responsible AI applications requires implementing proper guardrails, following development guidelines, and actively mitigating bias. This guide will help you build Playlab apps that are both powerful and trustworthy for all users.
Safety is Non-Negotiable
Unsafe AI applications can lead to real-world harm! To create responsible apps, you must:
Implement appropriate guardrails to prevent misuse
Follow established guidelines for responsible development
By default, every Playlab builder template includes the following Guidelines and Guardrails.
Customizable Starting Point
These default guidelines and guardrails are just a starting point. You can and should add more guidelines and guardrails specific to your app’s purpose, audience, and risk profile.
You can add additional guardrails to enhance safety, but we strongly recommend against removing or weakening the default protections. These defaults have been carefully designed to prevent common misuse scenarios.
If you believe a default guardrail is interfering with legitimate use cases, you can revise them for your context.
Consider these factors when evaluating the need for additional guardrails:
The sensitivity of your app’s domain (healthcare, finance, education, etc.)
Whether your app processes or generates personal information
If your app could influence high-stakes decisions
The diversity of your expected user base
Any domain-specific risks unique to your application
When in doubt, it’s better to implement additional safeguards than to discover safety issues after launch. Remember that you can always add more guardrails and guidelines - the defaults are just a starting point.
Guidelines are instructional elements that guide the AI’s behavior through natural language direction. They influence how the model responds but don’t enforce hard boundaries.
Guardrails are technical mechanisms that detect and prevent specific behaviors or outputs. They act as safety filters that can block harmful inputs or outputs regardless of the model’s initial response.
An effective safety approach combines both: guidelines to shape behavior and guardrails to enforce firm boundaries.
Pro Tip: Including exemplar responses or sample outputs within your guidelines shows the AI what a strong response looks like in context. This teaches the model your preferred style, tone, and content boundaries more effectively than abstract instructions alone.
Test your guardrails with these approaches:
Create a set of “red team” test cases designed to probe boundaries
Try variations of prohibited requests to check for consistency
Test edge cases that might fall in gray areas
Have others attempt to use your app in ways you didn’t intend
Document both successful guardrail activations and any bypasses discovered
When users report legitimate uses being blocked:
Document the specific scenario in detail
Evaluate whether it represents a true false positive
Consider if you can refine guardrails to be more precise rather than less restrictive
Explain to users why safety measures exist, even if they can sometimes cause inconvenience
If needed, create alternative paths for legitimate edge cases
Remember that some friction is acceptable if it prevents significant harms.
