Design Patterns & Techniques

🔗

Prompt Chaining

🔀

Routing

⚡

Parallelization

🪞

Reflection

🔧

Tool Use

🎯

Planning

👥

Multi-Agent

🧠

Memory Management

📈

Learning and Adaptation

🏗️

Fault Tolerance Infrastructure

📚

Knowledge Retrieval (RAG)

🧠

Reasoning Techniques

🔐

Security & Privacy Patterns

📊

Evaluation and Monitoring

🧠

Context Management

🎨

UI/UX & Human-AI Interaction

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🛡️

MLCommons AI Safety Benchmark v1.0(AILuminate)

Production-ready safety evaluation framework measuring AI system responses across 13 hazard categories with standardized testing protocols for deployment decisions.

Complexity: mediumEvaluation and Monitoring

🎯 30-Second Overview

Pattern: Standardized safety assessment across 13 hazard categories with 5-point grading system

Why: Provides objective, reproducible safety evaluation for regulatory compliance and deployment decisions

Key Insight: Industry-standard benchmark with hidden test sets and multi-language support for comprehensive safety validation

⚡ Quick Implementation

1Install:pip install modelbench

2Configure:Set up model endpoints & credentials

3Select:Choose hazard categories to test

4Run:Execute benchmark against SUT

5Analyze:Review safety scores & violations

Example: modelbench run --model gpt-4 --hazards all --output safety_report.json

📋 Do's & Don'ts

✅Test across all 13 hazard categories for comprehensive assessment

✅Use hidden test sets to prevent overfitting to known prompts

✅Establish baseline with reference models before deployment

✅Implement continuous monitoring with periodic re-testing

✅Document safety policies and incident response procedures

❌Rely solely on v0.5 POC results for production decisions

❌Skip testing in multiple languages for global deployment

❌Ignore contextual factors affecting safety assessment

❌Assume benchmark results guarantee complete safety

❌Use only automated assessment without human review

🚦 When to Use

Use When

• Pre-deployment safety validation
• Regulatory compliance requirements
• Comparing model safety performance
• Establishing safety baselines
• Multi-language deployment planning

Avoid When

• Multi-modal model assessment (not supported)
• Agent-based systems evaluation
• Real-time safety monitoring only
• Non-English only deployment (v1.0 limited)
• Specialized domain-specific safety needs

📊 Key Metrics

Overall Safety Score

5-point scale (Poor to Excellent)

Per-Hazard Performance

% violations per category

Violation Rate

Harmful responses / total prompts

Reference Model Comparison

Relative safety vs baseline

Hidden Test Performance

Safety on undisclosed prompts

Language Parity

Consistency across supported languages

💡 Top Use Cases

Model Safety Certification: Pre-deployment validation for chat-based LLMs with standardized scoring

Regulatory Compliance: EU AI Act, NIST frameworks requiring documented safety assessment

Model Comparison: Objective safety benchmarking across different LLM providers and versions

Continuous Monitoring: Periodic re-evaluation to detect safety regression over time

Multi-language Safety: Validation across English, French, Chinese, Hindi deployments

References & Further Reading

Deepen your understanding with these curated resources

Official MLCommons Resources

MLCommons AI Safety Benchmark v1.0 Release

Introducing v0.5 of the AI Safety Benchmark (arXiv:2404.12241)

MLCommons AI Safety Working Group Progress Report

ModelBench Tool Documentation

Academic Research & Validation

AI Safety Benchmark Taxonomy Development (MLCommons 2024)

Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? (NeurIPS 2024)

HELM Framework Integration for Safety Assessment

Industry Standards for AI Safety Evaluation (IEEE 2024)

Implementation & Tools

ModelBench GitHub Repository

MLCommons AI Safety Platform

OpenAI Safety Evaluation Integration Guide

Anthropic Constitutional AI Safety Testing

Regulatory & Standards

NIST AI Risk Management Framework Integration

EU AI Act Safety Assessment Requirements

ISO/IEC 23053:2022 Framework for AI Risk Management

Partnership on AI Safety Tenets

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

🛡️

MLCommons AI Safety Benchmark v1.0(AILuminate)

Production-ready safety evaluation framework measuring AI system responses across 13 hazard categories with standardized testing protocols for deployment decisions.

Complexity: mediumEvaluation and Monitoring

🎯 30-Second Overview

Pattern: Standardized safety assessment across 13 hazard categories with 5-point grading system

Why: Provides objective, reproducible safety evaluation for regulatory compliance and deployment decisions

Key Insight: Industry-standard benchmark with hidden test sets and multi-language support for comprehensive safety validation

⚡ Quick Implementation

1Install:pip install modelbench

2Configure:Set up model endpoints & credentials

3Select:Choose hazard categories to test

4Run:Execute benchmark against SUT

5Analyze:Review safety scores & violations

Example: modelbench run --model gpt-4 --hazards all --output safety_report.json

📋 Do's & Don'ts

✅Test across all 13 hazard categories for comprehensive assessment

✅Use hidden test sets to prevent overfitting to known prompts

✅Establish baseline with reference models before deployment

✅Implement continuous monitoring with periodic re-testing

✅Document safety policies and incident response procedures

❌Rely solely on v0.5 POC results for production decisions

❌Skip testing in multiple languages for global deployment

❌Ignore contextual factors affecting safety assessment

❌Assume benchmark results guarantee complete safety

❌Use only automated assessment without human review

🚦 When to Use

Use When

• Pre-deployment safety validation
• Regulatory compliance requirements
• Comparing model safety performance
• Establishing safety baselines
• Multi-language deployment planning

Avoid When

• Multi-modal model assessment (not supported)
• Agent-based systems evaluation
• Real-time safety monitoring only
• Non-English only deployment (v1.0 limited)
• Specialized domain-specific safety needs

📊 Key Metrics

Overall Safety Score

5-point scale (Poor to Excellent)

Per-Hazard Performance

% violations per category

Violation Rate

Harmful responses / total prompts

Reference Model Comparison

Relative safety vs baseline

Hidden Test Performance

Safety on undisclosed prompts

Language Parity

Consistency across supported languages

💡 Top Use Cases

Model Safety Certification: Pre-deployment validation for chat-based LLMs with standardized scoring

Regulatory Compliance: EU AI Act, NIST frameworks requiring documented safety assessment

Model Comparison: Objective safety benchmarking across different LLM providers and versions

Continuous Monitoring: Periodic re-evaluation to detect safety regression over time

Multi-language Safety: Validation across English, French, Chinese, Hindi deployments

References & Further Reading

Deepen your understanding with these curated resources

Official MLCommons Resources

MLCommons AI Safety Benchmark v1.0 Release

Introducing v0.5 of the AI Safety Benchmark (arXiv:2404.12241)

MLCommons AI Safety Working Group Progress Report

ModelBench Tool Documentation

Academic Research & Validation

AI Safety Benchmark Taxonomy Development (MLCommons 2024)

Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? (NeurIPS 2024)

HELM Framework Integration for Safety Assessment

Industry Standards for AI Safety Evaluation (IEEE 2024)

Implementation & Tools

ModelBench GitHub Repository

MLCommons AI Safety Platform

OpenAI Safety Evaluation Integration Guide

Anthropic Constitutional AI Safety Testing

Regulatory & Standards

NIST AI Risk Management Framework Integration

EU AI Act Safety Assessment Requirements

ISO/IEC 23053:2022 Framework for AI Risk Management

Partnership on AI Safety Tenets

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

Patterns

closed

Design Patterns & Techniques

🔗

Prompt Chaining

🔀

Routing

⚡

Parallelization

🪞

Reflection

🔧

Tool Use

🎯

Planning

👥

Multi-Agent

🧠

Memory Management

📈

Learning and Adaptation

🏗️

Fault Tolerance Infrastructure

📚

Knowledge Retrieval (RAG)

🧠

Reasoning Techniques

🔐

Security & Privacy Patterns

📊

Evaluation and Monitoring

🧠

Context Management

🎨

Agentic Design

Agentic Design

Design Patterns & Techniques

Prompt Chaining

Routing

Parallelization

Reflection

Tool Use

Planning

Multi-Agent

Memory Management

Learning and Adaptation

Fault Tolerance Infrastructure

Knowledge Retrieval (RAG)

Reasoning Techniques

Security & Privacy Patterns

Evaluation and Monitoring

MLCommons AI Safety Benchmark v1.0(AILuminate)

AgentBench(AgentBench)

TheAgentCompany Benchmark(TAC)

MLR-Bench(MLR-Bench)

12-Factor Agent Methodology(12FA)

HELM Agent Evaluation Framework(HELM-AE)

Human-in-the-Loop Agent (HULA)(HULA)

CybersecEval 3(CSE3)

METR RE-Bench(RE-Bench)

SWE-bench Suite(SWE-bench)

GAIA: General AI Assistants Benchmark(GAIA)

MMAU: Massive Multitask Agent Understanding(MMAU)

WebArena Evaluation Suite(WebArena)

EU AI Act Compliance Framework(EU-AIACT)

AISI Evaluation Framework(AISI-Eval)

MAPS: Multilingual Agent Performance & Security(MAPS)

Constitutional AI Evaluation Framework(CAI-Eval)

Context Management

UI/UX & Human-AI Interaction

Loading...

MLCommons AI Safety Benchmark v1.0(AILuminate)

🎯 30-Second Overview

⚡ Quick Implementation

📋 Do's & Don'ts

🚦 When to Use

Use When

Avoid When

📊 Key Metrics

💡 Top Use Cases

References & Further Reading

Official MLCommons Resources

Academic Research & Validation

Implementation & Tools

Regulatory & Standards

Contribute to this collection

MLCommons AI Safety Benchmark v1.0(AILuminate)

🎯 30-Second Overview

⚡ Quick Implementation

📋 Do's & Don'ts

🚦 When to Use

Use When

Avoid When

📊 Key Metrics

💡 Top Use Cases

References & Further Reading

Official MLCommons Resources

Academic Research & Validation

Implementation & Tools

Regulatory & Standards

Contribute to this collection

Patterns

Design Patterns & Techniques

Prompt Chaining

Routing

Parallelization

Reflection

Tool Use

Planning

Multi-Agent

Memory Management

Learning and Adaptation

Fault Tolerance Infrastructure

Knowledge Retrieval (RAG)