Analyzing System Prompts: Insights from Claude by Anthropic
The system prompt of a model, such as Claude from Anthropic, is a set of instructions that guides its behavior even before the user asks the first question. Analyzing this prompt provides valuable insights into creating effective instructions for language models.
Why Analyze System Prompts?
Understanding how the system prompts of existing models work is crucial because it:
- Reveals how precise and structured instructions must be to achieve predictable results.
- Shows techniques for defining behaviors, including ethical and safety guidelines.
- Illustrates the importance of context, examplesand tool usage in shaping AI responses.
- Provides practical knowledge about prompt engineering that can be applied when working with various AI models.
Key Elements and Conclusions from Claude's System Prompt
Analyzing Claude's system prompt reveals several key elements and strategies used to guide the model:
Precision and Structure of Instructions
The foundation of an effective prompt is its detail and organization.
- Precision: Claude's prompt avoids generalities, defining behaviors in many scenarios, including rules for using tools and interpreting results. Ambiguities are eliminated.
- Structure: The use of sections, headings, listsand XML tags (e.g.,
<citation_instructions>,<search_instructions>) helps the model process complex directives. Good organization is key. - Result Formatting: The prompt specifies the required format of responses, such as Markdown, LaTeX formulas, or specific citation formats (
<antml:cite>), as well as the creation of artifacts with specific MIME types.
Defining Desired and Undesired Behaviors
Clear guidelines on what the model should do and what it should avoid are essential.
- Strong Directives: The use of keywords like MUST, ALWAYS, NEVER, DO NOT (e.g., "NEVER reproduces any copyrighted material...") leaves no room for interpretation.
- Safety and Ethics: This is a priority. Rules are defined regarding avoiding harmful, copyrightedand illegal content, as well as limitations (e.g., not providing medical advice without disclaimers). Sections like
<harmful_content_safety>and<mandatory_copyright_requirements>address this.
Learning Through Examples and Providing Context
The model is provided with specific patterns and frameworks for action.
- Examples: Sections like
<search_examples>contain question-answer pairs illustrating expected behavior, tool usageand reliance on internal knowledge. - Context and Role: Defining the role ("You are Claude, created by Anthropic"), the knowledge cut-off date, or information about current events places the model's actions within a specific framework and influences the style of responses.
Integration with Tools and Complexity Management
The prompt must precisely define how the AI should interact with external resources and handle difficult situations.
- Instructions for Tools: Rules regarding the use of APIs, search engines (web_search, google_drive_search), or databases must be crystal clear: call format, parameters, interpretation of results, decision-making logic.
- Managing Uncertainty: The prompt anticipates situations where a query is too complex or the model is unsure of the answer. Categories of query complexity are defined (e.g.,
<never_search_category>,<research_category>) along with action strategies, including informing about the possibility of hallucination. - Iterative Content Creation: The model can be programmed to iteratively create or modify content (e.g., update, rewrite commands for artifacts).
Adaptability and Reinforcing Key Principles
Instructions can account for dynamic changes and reinforce the most important rules.
- Flexibility: The model learns to adapt to user preferences or conversation conditions thanks to sections like
<preferences_info>and<styles_info>, which define how to interpret these settings. - Critical Reminders: At the end of a long prompt, condensed reminders of absolutely key principles are placed (sections
<critical_reminders>,<search_reminders>) to ensure they are not overlooked.
Summary
In conclusion, the analysis of Claude's system prompt shows that effective prompt engineering relies on precision, structure, clear guidelines, examples, context, safety considerationsand thoughtful handling of tools and uncertainty.