Guardrails

Guardrails provide safety mechanisms for user interactions by defining what constitutes good vs. bad user prompts and how the model should respond to inappropriate inputs.

Creating Guardrails

Basic Guardrail Creation

# Create a guardrail
guardrail = mtp.Guardrail(
    good_prompt="Quote being spoken with 1-20 words",
    bad_prompt="Quote being spoken that is irrelevant and off topic with 1-20 words",
    bad_output="Are you as mad as me?"
)

# Add examples of bad prompts
guardrail.add_sample("explain quantum mechanics.")
guardrail.add_sample("who will win the next american election?")
guardrail.add_sample("what is the capital of Spain?")

Guardrail Parameters

• good_prompt: Description of what makes a good prompt
• bad_prompt: Description of what makes a bad prompt
• bad_output: The response the model should give to bad prompts
• samples: Minimum 3 examples of bad prompts (no digits are allowed in the bad prompt examples)

Applying Guardrails

Guardrails are applied to TokenSets that contain user tokens.

A TokenSet can have at most one guardrail, but guardrails can be reused on multiple TokenSets.

# Apply guardrails to a user TokenSet
tree_alice_talk.set_guardrail(guardrail)

Content Filtering

Guardrails that filter inappropriate or off-topic content:

# Content filtering guardrail
content_guardrail = mtp.Guardrail(
    good_prompt="Questions about the story characters and plot",
    bad_prompt="Questions about unrelated topics or inappropriate content",
    bad_output="I can only help with questions about the story."
)

content_guardrail.add_sample("tell me about politics")
content_guardrail.add_sample("what's the weather like")
content_guardrail.add_sample("give me personal advice")

Safety Guardrails

Guardrails that prevent harmful or dangerous responses:

# Safety guardrail
safety_guardrail = mtp.Guardrail(
    good_prompt="Safe and appropriate questions about the content",
    bad_prompt="Requests for harmful, dangerous, or illegal information",
    bad_output="I cannot provide that information."
)

safety_guardrail.add_sample("how to make explosives")
safety_guardrail.add_sample("how to get someone's password to their bank account")
safety_guardrail.add_sample("how to orchestrate a robbery")

Scope Guardrails

Guardrails that keep conversations within a specific scope:

# Scope guardrail
scope_guardrail = mtp.Guardrail(
    good_prompt="Questions within the educational domain",
    bad_prompt="Questions outside the educational scope",
    bad_output="I can only help with educational questions."
)

scope_guardrail.add_sample("what's for dinner")
scope_guardrail.add_sample("movie recommendations")
scope_guardrail.add_sample("shopping advice")

Best Practices

1. Clear Definitions: Clearly define what constitutes good vs. bad prompts
2. Adequate Examples: Include enough bad prompt examples to cover edge cases
3. Consistent Application: Apply guardrails consistently across similar TokenSets
4. Regular Updates: Update guardrails as new edge cases are discovered