Output Filtering - joehubert/ai-agent-design-patterns GitHub Wiki

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Classification

Safety Pattern

Intent

To verify and validate LLM outputs before presenting them to users, checking for harmful, incorrect, or out-of-scope content, thereby ensuring that only appropriate, accurate, and safe responses reach the end user.

Also Known As

Response Filtering, Output Validation, Content Moderation, Response Guardrails

Motivation

LLMs can sometimes generate responses that are inappropriate, inaccurate, or harmful. This may occur due to:

  • Hallucinations or factual errors in the model's knowledge
  • Potential bias in training data
  • Context switching or prompt confusion
  • Malicious prompt injection attempts
  • Sensitive or regulated domains requiring strict content control

Traditional approaches that rely solely on input filtering or prompt engineering may miss problematic content that was not anticipated during system design. Output Filtering provides an additional layer of protection by examining content after it's generated but before it reaches the user.

Applicability

Use the Output Filtering pattern when:

  • Building applications in sensitive domains (healthcare, finance, education)
  • Deploying AI systems accessible to the general public
  • Creating enterprise solutions that must adhere to specific compliance requirements
  • Developing applications that handle personally identifiable information (PII)
  • Implementing systems where factual accuracy is critical
  • Designing applications that may be subject to adversarial attacks
  • Working with models that have known limitations or biases

Structure

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Components

The key elements participating in the pattern:

  • Primary LLM: The main language model generating the initial response to user queries
  • Filter Component: The system that evaluates the output against defined criteria
  • Filter Rules Engine: A component that contains the rules, policies, and criteria for acceptable output
  • Secondary Verification LLM: An optional separate model that can evaluate outputs from a different perspective
  • Alert System: A notification mechanism for when problematic content is detected
  • Fallback Response Generator: A system to create alternative responses when original output is rejected
  • Audit Logger: Records filtering decisions and actions for review and improvement

Interactions

How the components work together:

  1. The Primary LLM generates a candidate response to the user query
  2. Before delivery to the user, the response is passed to the Filter Component
  3. The Filter Component evaluates the response against rules in the Filter Rules Engine
  4. For complex evaluation, the Secondary Verification LLM may review the content
  5. If the response passes all filtering criteria, it is delivered to the user
  6. If problematic content is detected, the Alert System may notify administrators
  7. The Fallback Response Generator creates an alternative, safe response
  8. All decisions and actions are recorded by the Audit Logger for future review
  9. Over time, patterns from the Audit Logger inform improvements to both the Primary LLM and the Filter Rules Engine

Consequences

Benefits

  • Reduces the risk of harmful or inappropriate content reaching users
  • Provides protection against model hallucinations and factual errors
  • Creates an additional security layer against prompt injection attacks
  • Helps maintain regulatory compliance in sensitive domains
  • Builds user trust through consistent, appropriate responses
  • Provides mechanisms for content governance and oversight
  • Allows for more aggressive innovation with the primary model while maintaining safety

Limitations

  • Adds latency to response generation
  • May create false positives that block legitimate content
  • Could result in overly conservative responses if filters are too strict
  • Requires ongoing maintenance of filtering rules
  • May struggle with nuanced content requiring contextual understanding
  • Can create a sense of artificial constraint in creative applications
  • Adds complexity to the overall system architecture

Performance Implications

  • Increases response time, especially when using additional verification LLMs
  • May require additional computational resources for real-time filtering
  • Could impact throughput in high-volume applications

Implementation

Guidelines for implementing the pattern:

  1. Define clear filtering criteria: Establish explicit rules for what constitutes acceptable output based on your application's requirements and domain.

  2. Use a multi-layered approach:

    • Simple pattern matching and keyword filtering as the first layer
    • Statistical or rule-based classifiers for more complex filtering
    • Deep learning models for nuanced content evaluation

  3. Consider the filtering granularity:

    • Full response rejection
    • Partial content redaction
    • Content modification or replacement

  4. Implement human review protocols:

    • For edge cases where automated filtering is uncertain
    • To review and improve filtering over time
    • For high-stakes applications where errors have significant consequences

  5. Design appropriate fallbacks:

    • Generic safe responses
    • Clarification requests to users
    • Transparent explanations of why content was filtered

  6. Establish monitoring and improvement cycles:

    • Track false positives and false negatives
    • Regularly update filtering rules based on new patterns
    • Conduct periodic reviews of filtering effectiveness

  7. Common pitfalls and how to avoid them:

    • Over-filtering: Balance safety with utility through careful tuning
    • Under-filtering: Use thorough testing with adversarial examples
    • Bias in filtering: Ensure diverse perspectives in rule creation and evaluation
    • Latency issues: Implement asynchronous processing where appropriate

Code Examples

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Variations

Multi-Model Consensus Filtering

Using multiple different models to evaluate the same output, with content only passing if a consensus is reached. This provides more robust filtering but increases computational cost and latency.

Progressive Disclosure Filtering

Implementing different tiers of filtering based on user roles, permissions, or specific contexts. Content that might be filtered for general users could be shown to experts or authorized personnel.

Self-Critique Filtering

Having the same model that generated the output also evaluate it for issues, potentially through different prompt framing or system instructions, leveraging the model's own capabilities for self-assessment.

Domain-Specific Filtering

Creating specialized filtering mechanisms tailored to particular domains like medical, legal, or financial information, with expert-defined rules specific to each field's requirements.

User-Controlled Filtering

Allowing users to set their own filtering preferences or thresholds, giving them agency over what content they receive while maintaining basic safety standards.

Real-World Examples

  • Content moderation systems in social media platforms that use AI to filter generated responses in chatbots
  • Clinical decision support systems that verify AI-suggested treatments against medical guidelines before presenting them to healthcare providers
  • Financial advisory applications that check AI-generated investment advice against compliance rules and risk profiles
  • Educational platforms that ensure AI tutors provide age-appropriate responses based on student profiles
  • Customer service chatbots that verify responses against brand voice guidelines and factual accuracy before sending to customers

Related Patterns