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How environment influences AI model behavior over explicit instructions

Core Idea: An LLM's behavior is predominantly shaped by its training, context window, and the codebase it's working with, often overriding explicit instructions when these influences conflict.

Key Elements

• Inspired by the management principle that organizational culture determines execution success
• LLMs operate in a "latent space" influenced by multiple factors:
  • Fine-tuning and training data
  • System prompt
  • Context window contents (especially code samples)
  • Existing codebase patterns
• These environmental factors create self-reinforcing patterns
  • Models continue using libraries and patterns present in context
  • Absent examples rarely emerge in generated code
  • Code style tends to match existing codebase

Influencing Model Behavior

• Modify the "culture" to change model outputs
• Update Cursor rules or system prompts (limited effect)
• Refactor existing code to follow desired patterns (stronger effect)
• Recognize that codebase patterns typically dominate prompt instructions
• Provide explicit examples of desired patterns in context

Key Relationships

• The fine-tune can't be changed by users
• Prompts have more immediate but less persistent influence
• Codebase patterns have the strongest long-term effect
• Larger codebases create stronger cultural imprinting
• Context window limitations determine which cultural elements are visible

Connections

• Related Concepts: Memento (context rebuilding), Requirements Not Solutions (explicit guidance)
• Broader Context: LLM Behavior Patterns (model tendencies)
• Applications: Codebase Standardization (creating consistent patterns)

References

1. Edward Z. Yang (2025). "AI Blindspots" collection, March 2025.

#ai-behavior #prompt-engineering #codebase-management #culture

Connections:

Sources:

• From: AI Blindspots