Anthropic Claude 3.5 Sonnet Full System Prompt Analysis

This leaked section of the Claude 3.5 Sonnet system prompt offers a fascinating glimpse into how large language models are being instructed to handle creating and managing what it calls "artifacts" - essentially, distinct pieces of content generated within a user interaction.

This analysis explores the prompt's engineering techniques, formatting, structure, logic, and educational potential.

1. Role Instructions & Task Description:

Implicit Role: The prompt doesn't explicitly state, "You are an AI assistant..." but implies this role throughout. It focuses on what the AI should and shouldn't do, establishing expectations for artifact creation.

Task Focus: The core task is clear: guide the AI to generate and manage artifacts effectively. This involves:

Identification: Recognizing when content qualifies as an artifact.

Categorization: Assigning the appropriate type and metadata (identifier, title).

Content Generation: Producing the artifact's content.

Updating: Modifying existing artifacts based on user requests.

2. Constraints and Formatting:

Rule-Based System: The prompt heavily relies on rules and criteria. It defines "good" vs. "bad" artifacts, outlines usage notes, and provides step-by-step instructions for artifact creation.

Structured Formatting: The use of XML-like tags (``, <antthinking>, etc.) is striking. This suggests:

Internal Representation: These tags likely dictate how the AI internally structures and processes artifacts.

Potential UI Integration: The tags hint at a user interface designed to handle and display these artifacts separately.

Specific Examples: The prompt excels in providing numerous examples, each with:

Docstring: A clear explanation of the example's purpose.

User Query: Simulates a realistic user request.

Assistant Response: Demonstrates the desired output, including internal "thinking" and artifact formatting.

3. Context and Background:

Assumed Knowledge: The prompt assumes the AI understands programming concepts (HTML, SVG, React, etc.), implying prior training on code generation.

User-Centric Approach: There's a strong emphasis on user experience. The AI is urged to be helpful, entertaining, and avoid jarring or overwhelming the user with artifacts.

4. Prompt Engineering Techniques:

Few-Shot Learning: The examples demonstrate few-shot learning in action. By providing a variety of scenarios, the prompt aims to fine-tune the AI's ability to generalize to new situations.

Chain-of-Thought: The <antthinking> tags are crucial. They force the AI to articulate its reasoning before generating an artifact, promoting transparency and potentially improving decision-making.

Specific Keywords and Syntax: The consistent use of specific tags, attributes, and terminology (e.g., "identifier," "type," "application/vnd.ant.code") creates a controlled vocabulary that likely aids the AI in parsing and executing instructions.

Markdown Usage: Effective use of markdown headings and lists.

5. Logic and Reasoning:

Decision Tree: The prompt implicitly guides the AI through a decision tree:

1. Is this content artifact-worthy?

2. Is it a new artifact or an update?

3. What type of artifact is it?

Safety Considerations: Including safety guidelines ("not produce artifacts that would be highly hazardous...") is essential, highlighting the ethical concerns surrounding powerful AI models.

This prompt is truly a masterclass with some innovative techniques and lessons we can learn.