How to Implement Constitutional Classifiers to Harden Your LLM API Against Jailbreaks
Add a two-stage constitutional classifier layer to your FastAPI endpoint — blocks 95%+ of jailbreaks at ~1% extra compute cost per Anthropic’s research.
AI Safety and Ethics
How to Build Adversarial Robustness Testing for Vision Models
Evaluate your vision models against adversarial perturbations before deployment using the Adversarial Robustness Toolbox
How to Build Adversarial Test Suites for ML Models
Test your ML models against adversarial inputs, distribution shifts, and edge cases before they fail in production
How to Build AI Model Unlearning Pipelines for Data Removal Requests
Handle right-to-be-forgotten requests by building model unlearning pipelines with gradient ascent and fine-tuning
How to Build an AI Incident Response and Rollback System
Detect failing AI models fast and roll them back automatically before bad predictions reach your users
How to Build Automated Age and Content Gating for AI Applications
Add age-appropriate content filters to your AI app with keyword screening, ML classification, and LLM-based review
How to Build Automated Bias Audits for LLM Outputs
Catch demographic biases in your LLM app before users do with automated prompt-based auditing
How to Build Automated Data Retention and Deletion for AI Systems
Automate GDPR-compliant data retention with time-based deletion, audit trails, and dry-run support
How to Build Automated Fairness Testing for LLM-Generated Content
Test LLM outputs for demographic bias with automated fairness checks using Python and statistical analysis
How to Build Automated Hate Speech Detection with Guardrails
Build a multi-layer hate speech detection system with classifier models and content filtering rules.
How to Build Automated Jailbreak Detection for LLM Applications
Detect prompt injection and jailbreak attempts before they reach your LLM with a multi-layer detection pipeline
How to Build Automated Output Safety Classifiers for LLM Apps
Catch unsafe LLM outputs before they reach users with classifier pipelines that flag toxicity, bias, and policy violations.
How to Build Automated PII Redaction Testing for LLM Outputs
Catch PII leaks in LLM outputs automatically with a testing framework built on Presidio and pytest
How to Build Automated Prompt Leakage Detection for LLM Apps
Build automated tests that catch prompt leakage before it reaches production with Python and regex guards
How to Build Automated Stereotype Detection for LLM Outputs
Catch stereotypical patterns in your LLM app with automated test suites based on StereoSet-style probing
How to Build Automated Toxicity Detection for User-Generated Content
Detect toxic comments in production with a multi-model ensemble that catches what single classifiers miss
How to Build Consent and Data Rights Management for AI Systems
Implement user consent tracking, data deletion requests, and opt-out workflows for AI training pipelines.
How to Build Copyright Detection for AI Training Data
Detect and flag potentially copyrighted text in training datasets with n-gram fingerprinting and fuzzy matching
How to Build Differential Privacy Testing for LLM Training Data
Verify your training data meets privacy guarantees by building DP tests with Opacus and membership inference
How to Build Fairness-Aware ML Pipelines with Fairlearn
Use Fairlearn to measure demographic parity, equalized odds, and fix biased classifiers in Python
How to Build Hallucination Scoring and Grounding Verification for LLMs
Catch LLM hallucinations automatically by scoring generated text against source documents using NLI-based verification
How to Build Input and Output Guardrails for Multi-Agent Systems
Protect multi-agent systems from prompt injection and harmful outputs with boundary guardrails at each agent step
How to Build LLM Output Filtering with Guardrails and Validators
Stop bad LLM outputs before they reach users with validators, PII detection, and automatic retries
How to Build Membership Inference Attack Detection for ML Models
Audit whether your ML model leaks training data by building a membership inference detector from scratch with Python
How to Build Model Cards and Document AI Systems Responsibly
Write clear model documentation that helps users understand what your AI can and can’t do safely
How to Build Output Grounding and Fact-Checking for LLM Apps
Stop hallucinations before they reach users with claim-level grounding checks and NLI-based verification for RAG apps.
How to Build Prompt Injection Detection for LLM Apps
Protect your LLM applications from prompt injection attacks with regex filters, fine-tuned classifiers, and production-ready middleware.
How to Build Rate Limiting and Abuse Prevention for LLM APIs
Protect your LLM API from abuse with per-user rate limits, token counting, and cost controls using Redis
How to Build Reproducible AI Experiments with Deterministic Seeds
Set up proper random seeds to get identical results every time you run your ML training pipeline
How to Build Watermark Detection for AI-Generated Images
Identify AI-generated images by detecting invisible watermarks embedded by diffusion models
How to Implement AI Audit Logging and Compliance Tracking
Track every AI decision your system makes with structured audit logs for regulatory compliance and debugging
How to Implement AI Output Citations and Source Attribution
Build citation pipelines that map every AI-generated claim back to its source, so users can trust and verify your app’s answers.
How to Implement Constitutional AI Principles in LLM Applications
Ship safer AI apps by teaching models to critique and revise their own outputs using Constitutional AI patterns and Python.
How to Implement Federated Learning for Privacy-Preserving ML
Train ML models across multiple clients without sharing raw data using Flower’s FedAvg strategy and differential privacy
How to Run Automated AI Safety Benchmarks on LLMs
Test your LLM for safety issues before deployment with automated benchmarks for toxicity, bias, refusals, and jailbreak resistance.
How to Add Guardrails to LLM Apps with NeMo Guardrails
Protect your LLM application from jailbreaks, off-topic use, and harmful outputs in under 50 lines
How to Build Explainable ML Models with SHAP and LIME
Build transparent ML models by adding SHAP and LIME explanations to credit scoring, tree models, and neural networks.
How to Detect AI-Generated Text with Watermarking
Apply statistical watermarks during text generation and verify them with open-source detectors in Python
How to Detect and Mitigate Bias in ML Models
Find and fix unfair predictions in your ML pipeline with MetricFrame, ThresholdOptimizer, and ExponentiatedGradient
How to Detect and Redact PII in Text with Presidio and Python
Strip sensitive data from text in a few lines of Python using Presidio’s analyzer and anonymizer engines.
How to Detect and Reduce Hallucinations in LLM Applications
Stop your LLM app from making things up with practical detection methods, validators, and grounding strategies you can ship today.
How to Implement Content Filtering for LLM Applications
Add safety guardrails to your AI application with input validation and output filtering
How to Interpret LLM Decisions with Attention Visualization
See exactly which tokens your model focuses on and build explainability reports for stakeholders
How to Red-Team LLM Applications
Find prompt injection, jailbreak, and data extraction vulnerabilities in your AI app with red-teaming tools
How to Train ML Models with Differential Privacy Using Opacus
Add formal privacy guarantees to your deep learning pipeline with three lines of Opacus code and smart epsilon budgeting