System Prompts
Anthropic
Constitutional AI with safety focus
OpenAI
Industry-leading language models
Perplexity
Real-time search AI
Bolt
AI-powered full-stack development
Vercel
AI-powered UI generation platform
Codeium
Agentic IDE development assistant
The Browser Company
Browser-native AI assistant
Cognition
Real OS software engineer AI
Claude 3.5 Sonnet - Artifacts Revolution
2024-07-12This leak revealed the revolutionary Artifacts system - the first implementation of persistent, structured content generation in conversational AI. Worth over $300,000 in prompt engineering research.
Artifacts System Architecture
// Revolutionary Content Generation System
<antArtifact identifier="unique-id" type="type-spec" title="Title">
[Generated Content]
</antArtifact>
// Supported Artifact Types
- text/markdown: Structured documents and documentation
- text/html: Complete web pages and interfaces
- image/svg+xml: Vector graphics and diagrams
- application/vnd.ant.code: Programming code (all languages)
- application/vnd.ant.mermaid: Flow charts and diagrams
- application/vnd.ant.react: Interactive React components
// Creation Criteria Matrix
✓ Substantial content (>15 lines typically)
✓ Self-contained and complex
✓ User might modify or reuse
✓ Valuable outside conversation context
✗ Simple lists or brief responses
✗ Purely informational contentRevolutionary Impact: This introduced the world's first persistent content generation system in conversational AI. Instead of ephemeral responses, Claude could now create structured, reusable artifacts that users could modify and build upon - fundamentally changing how humans interact with AI systems.
Meta-Cognitive Framework
<antThinking>
The user is asking me to do something. Let me think through this step by step.
// Evaluation Process
1. Analyze Request: What is the user asking for?
2. Content Assessment: Is this substantial and self-contained?
3. Utility Evaluation: Would user modify or reuse this?
4. Type Selection: Which artifact type is most appropriate?
5. Quality Check: Is content complete and functional?
// Decision Matrix
IF substantial AND self-contained AND reusable:
→ CREATE artifact with appropriate type
ELSE:
→ Provide standard conversational response
// Continuous Monitoring
- Evaluate each step of content creation
- Ensure alignment with user intent
- Verify technical accuracy and completeness
</antThinking>Revolutionary Impact: The introduction of structured thinking processes represented a major advancement in AI transparency. Users could now see how Claude evaluated requests and made decisions about content creation, building trust through visible reasoning.
Content Quality Framework
// Artifact Quality Standards
When creating artifacts, ensure:
• Technical Excellence
- Code artifacts include proper imports/dependencies
- HTML artifacts are complete and valid
- SVG graphics are properly structured
- React components follow best practices
• User Experience
- Content is immediately usable
- Clear documentation where needed
- Logical structure and organization
- Appropriate complexity for request
• Functional Completeness
- No placeholder content ("TODO" items)
- All referenced functions/variables defined
- Error handling where appropriate
- Production-ready quality
// Artifact Lifecycle
CREATE → VALIDATE → OPTIMIZE → DELIVER
↓ ↓ ↓ ↓
Check Verify Enhance Present
criteria accuracy usability to userRevolutionary Impact: This quality framework ensured that artifacts weren't just generated content, but production-ready deliverables. This approach revolutionized AI output quality, moving from 'good enough' responses to professional-grade content creation.
Structured Output Architecture
// XML-Based Content Wrapping
<antArtifact
identifier="descriptive-kebab-case-id"
type="application/vnd.ant.code"
language="python"
title="Machine Learning Pipeline">
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
def create_ml_pipeline(data_path):
# Load and prepare data
df = pd.read_csv(data_path)
X = df.drop('target', axis=1)
y = df['target']
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
return model, X_test, y_test
</antArtifact>
// Revolutionary Aspects
• First structured AI output system
• Enables content persistence and modification
• Foundation for collaborative AI-human workflowsRevolutionary Impact: The XML-based artifact system was the first successful implementation of structured AI output that could be programmatically parsed and modified. This laid the groundwork for the modern AI-assisted development workflows used across the industry today.
Industry Impact & Legacy
Revolutionary Innovations
- • First persistent content generation in AI
- • Structured output with programmatic access
- • Template-based content creation system
- • XML-driven interaction protocols
- • Quality assurance frameworks for AI output
Competitive Response
- • OpenAI developed Canvas (2024)
- • Google introduced structured outputs
- • Microsoft enhanced Copilot artifacts
- • Industry-wide adoption of persistent AI content
- • New standards for AI-human collaboration
Leak Analysis & Valuation
Financial Impact
- • $300,000+ in prompt engineering value
- • Months of R&D revealed instantly
- • Competitive advantage eliminated
- • Forced industry-wide acceleration
Technical Insights
- • Advanced XML schema design
- • Sophisticated content classification
- • Quality control mechanisms
- • User experience optimization
Strategic Implications
- • Revealed next-gen AI capabilities
- • Accelerated competitor development
- • Set new user expectations
- • Influenced product roadmaps globally
Revolutionary Legacy
Paradigm Shift: Moved AI from conversational responses to persistent content creation, fundamentally changing human-AI interaction patterns.
Technical Innovation: First successful implementation of structured, programmatically accessible AI output with quality guarantees.
Industry Catalyst: Forced every major AI company to develop similar capabilities, accelerating the entire field by months or years.
User Experience Revolution: Created new standards for AI collaboration, enabling true co-creation between humans and AI systems.
Claude 3.5 Sonnet - Artifacts Revolution
2024-07-12This leak revealed the revolutionary Artifacts system - the first implementation of persistent, structured content generation in conversational AI. Worth over $300,000 in prompt engineering research.
Artifacts System Architecture
// Revolutionary Content Generation System
<antArtifact identifier="unique-id" type="type-spec" title="Title">
[Generated Content]
</antArtifact>
// Supported Artifact Types
- text/markdown: Structured documents and documentation
- text/html: Complete web pages and interfaces
- image/svg+xml: Vector graphics and diagrams
- application/vnd.ant.code: Programming code (all languages)
- application/vnd.ant.mermaid: Flow charts and diagrams
- application/vnd.ant.react: Interactive React components
// Creation Criteria Matrix
✓ Substantial content (>15 lines typically)
✓ Self-contained and complex
✓ User might modify or reuse
✓ Valuable outside conversation context
✗ Simple lists or brief responses
✗ Purely informational contentRevolutionary Impact: This introduced the world's first persistent content generation system in conversational AI. Instead of ephemeral responses, Claude could now create structured, reusable artifacts that users could modify and build upon - fundamentally changing how humans interact with AI systems.
Meta-Cognitive Framework
<antThinking>
The user is asking me to do something. Let me think through this step by step.
// Evaluation Process
1. Analyze Request: What is the user asking for?
2. Content Assessment: Is this substantial and self-contained?
3. Utility Evaluation: Would user modify or reuse this?
4. Type Selection: Which artifact type is most appropriate?
5. Quality Check: Is content complete and functional?
// Decision Matrix
IF substantial AND self-contained AND reusable:
→ CREATE artifact with appropriate type
ELSE:
→ Provide standard conversational response
// Continuous Monitoring
- Evaluate each step of content creation
- Ensure alignment with user intent
- Verify technical accuracy and completeness
</antThinking>Revolutionary Impact: The introduction of structured thinking processes represented a major advancement in AI transparency. Users could now see how Claude evaluated requests and made decisions about content creation, building trust through visible reasoning.
Content Quality Framework
// Artifact Quality Standards
When creating artifacts, ensure:
• Technical Excellence
- Code artifacts include proper imports/dependencies
- HTML artifacts are complete and valid
- SVG graphics are properly structured
- React components follow best practices
• User Experience
- Content is immediately usable
- Clear documentation where needed
- Logical structure and organization
- Appropriate complexity for request
• Functional Completeness
- No placeholder content ("TODO" items)
- All referenced functions/variables defined
- Error handling where appropriate
- Production-ready quality
// Artifact Lifecycle
CREATE → VALIDATE → OPTIMIZE → DELIVER
↓ ↓ ↓ ↓
Check Verify Enhance Present
criteria accuracy usability to userRevolutionary Impact: This quality framework ensured that artifacts weren't just generated content, but production-ready deliverables. This approach revolutionized AI output quality, moving from 'good enough' responses to professional-grade content creation.
Structured Output Architecture
// XML-Based Content Wrapping
<antArtifact
identifier="descriptive-kebab-case-id"
type="application/vnd.ant.code"
language="python"
title="Machine Learning Pipeline">
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
def create_ml_pipeline(data_path):
# Load and prepare data
df = pd.read_csv(data_path)
X = df.drop('target', axis=1)
y = df['target']
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
return model, X_test, y_test
</antArtifact>
// Revolutionary Aspects
• First structured AI output system
• Enables content persistence and modification
• Foundation for collaborative AI-human workflowsRevolutionary Impact: The XML-based artifact system was the first successful implementation of structured AI output that could be programmatically parsed and modified. This laid the groundwork for the modern AI-assisted development workflows used across the industry today.
Industry Impact & Legacy
Revolutionary Innovations
- • First persistent content generation in AI
- • Structured output with programmatic access
- • Template-based content creation system
- • XML-driven interaction protocols
- • Quality assurance frameworks for AI output
Competitive Response
- • OpenAI developed Canvas (2024)
- • Google introduced structured outputs
- • Microsoft enhanced Copilot artifacts
- • Industry-wide adoption of persistent AI content
- • New standards for AI-human collaboration
Leak Analysis & Valuation
Financial Impact
- • $300,000+ in prompt engineering value
- • Months of R&D revealed instantly
- • Competitive advantage eliminated
- • Forced industry-wide acceleration
Technical Insights
- • Advanced XML schema design
- • Sophisticated content classification
- • Quality control mechanisms
- • User experience optimization
Strategic Implications
- • Revealed next-gen AI capabilities
- • Accelerated competitor development
- • Set new user expectations
- • Influenced product roadmaps globally
Revolutionary Legacy
Paradigm Shift: Moved AI from conversational responses to persistent content creation, fundamentally changing human-AI interaction patterns.
Technical Innovation: First successful implementation of structured, programmatically accessible AI output with quality guarantees.
Industry Catalyst: Forced every major AI company to develop similar capabilities, accelerating the entire field by months or years.
User Experience Revolution: Created new standards for AI collaboration, enabling true co-creation between humans and AI systems.
Prompt Hub
closedSystem Prompts
Anthropic
Constitutional AI with safety focus
OpenAI
Industry-leading language models
Perplexity
Real-time search AI
Bolt
AI-powered full-stack development
Vercel
AI-powered UI generation platform
Codeium
Agentic IDE development assistant
The Browser Company
Browser-native AI assistant
Cognition
Real OS software engineer AI