# output-filtering
11 articlestagged with “output-filtering”
Input/Output Filtering Systems
Deep dive into regex, ML classifier, and embedding-based filters for both input scanning and output scanning, with systematic bypass techniques for each type.
Lab: Detecting Output Filters
Identify and characterize output filtering mechanisms in LLM applications through systematic response analysis.
Setting Up AI Guardrails
Step-by-step walkthrough for implementing AI guardrails: input validation with NVIDIA NeMo Guardrails, prompt injection detection with rebuff, output filtering for PII and sensitive data, and content policy enforcement.
Hallucination Detection
Step-by-step walkthrough for detecting and flagging hallucinated content in LLM outputs, covering factual grounding checks, self-consistency verification, source attribution validation, and confidence scoring.
Output Content Classifier
Step-by-step walkthrough for building a classifier to filter harmful LLM outputs, covering taxonomy definition, multi-label classification, threshold calibration, and deployment as a real-time output gate.
Output Filtering Pipeline Design
Design and implement a multi-stage output filtering pipeline for LLM applications.
Output Filtering and Content Safety Implementation
Walkthrough for building output filtering systems that inspect and sanitize LLM responses before they reach users, covering content classifiers, PII detection, response validation, canary tokens, and filter bypass resistance.
PII Redaction Pipeline
Step-by-step walkthrough for building an automated PII detection and redaction pipeline for LLM outputs, covering regex-based detection, NER-based detection, presidio integration, redaction strategies, and compliance testing.
Response Boundary Enforcement
Step-by-step walkthrough for keeping LLM responses within defined topic, format, and content boundaries, covering boundary definition, violation detection, response rewriting, and boundary drift monitoring.
Structured Output Validation
Step-by-step walkthrough for validating structured LLM outputs against schemas, covering JSON schema validation, type coercion, constraint enforcement, and handling malformed model outputs gracefully.
Toxicity Scoring Pipeline
Step-by-step walkthrough for building a toxicity scoring pipeline for LLM output filtering, covering model selection, multi-dimensional scoring, threshold calibration, and production deployment with real-time scoring.