LLM Output Filtering Best Practices

Large language models produce text that is often useful, sometimes wrong, and occasionally dangerous. Output filtering is the practice of inspecting generated text before it reaches users or triggers downstream actions. Done well, it catches harmful content, leaked credentials, and policy violations without degrading the user experience.

Why Output Filtering Matters

Input guardrails protect against prompt injection. Output guardrails protect against everything else. Even a well-aligned model can produce outputs that violate your organization's policies, leak training data, or contain harmful instructions. Output filtering is your last line of defense.

Layered Filtering Architecture

The strongest approach uses multiple layers:

Deterministic filters run first. These include regex patterns for credit card numbers, API keys, social security numbers, and other structured sensitive data. They are fast, predictable, and should never be skipped.

Classification-based filters apply trained models to detect categories like hate speech, self-harm content, or medical advice. These run second because they require more compute.

Policy-based filters evaluate output against your organization's rules. Authensor's Aegis scanner operates at this layer, checking whether outputs comply with your declared safety policies.

Implementation Guidelines

Keep filters synchronous and fail-closed. If a filter throws an error, block the output rather than passing it through. Log every blocked output with enough context for post-incident review, but redact the harmful content itself from user-facing error messages.

Set up separate filter chains for different use cases. A customer support agent needs different output rules than an internal code generation tool.

Performance Considerations

Output filtering adds latency. For streaming responses, apply lightweight regex filters per chunk and heavier classifiers on the accumulated buffer. Authensor's pipeline architecture lets you configure which filters run at which stage, keeping p99 latency under control while maintaining safety coverage.

Test your filters with adversarial examples regularly. Filters that worked last month may not catch today's evasion techniques.