System Prompts & Instruction Design
System prompt patterns, instruction clarity, and behavioral control.
Behavioral Constraints and Rules
Behavioral constraints shape LLM behavior through specific, positively framed, and well-structured rules that achieve 15-20% better compliance when formatted as numbered lists rather than prose. Prerequisites: 04-system-prompts-and-instruction-design/system-prompt-anatomy.md, 01-foundations/how-llms-process-prompts.md
Dynamic System Prompts
Dynamic system prompts are assembled at runtime from modular components – including user roles, feature flags, time-sensitive context, and personalization slots – enabling applications to customize LLM behavior for each user and situation. Prerequisites: 04-system-prompts-and-instruction-design/system-prompt-anatomy.md, 04-system-prompts-and-instruction-design/behavioral-constraints-and-rules.md
Instruction Following and Compliance
LLM instruction compliance depends on instruction salience, formatting, position, and the model’s training-shaped attention budget, and understanding these factors enables systematic improvement of adherence rates. Prerequisites: 04-system-prompts-and-instruction-design/system-prompt-anatomy.md, 01-foundations/how-llms-process-prompts.md
Instruction Hierarchy Design
Instruction hierarchy establishes a chain of command – system over developer over user over tool data – that determines which instructions take priority when they conflict, serving as a primary defense against prompt injection. Prerequisites: 04-system-prompts-and-instruction-design/system-prompt-anatomy.md, 04-system-prompts-and-instruction-design/behavioral-constraints-and-rules.md
Meta-Prompting
Meta-prompting uses one LLM call to generate, refine, or optimize the prompt for another LLM call, creating a two-layer system where the model acts as its own prompt engineer. Prerequisites: 04-system-prompts-and-instruction-design/system-prompt-anatomy.md, 03-reasoning-elicitation/chain-of-thought-prompting.md
Multi-Turn Instruction Persistence
System prompt instructions lose effectiveness over long conversations, typically degrading after 20-30 turns, requiring active reinforcement techniques to maintain consistent model behavior. Prerequisites: 04-system-prompts-and-instruction-design/system-prompt-anatomy.md, 01-foundations/context-window-mechanics.md
Prompt Versioning and Management
Production prompts should be treated as code artifacts with version control, changelogs, regression testing, A/B testing infrastructure, and rollback procedures to ensure reliable, measurable, and reversible prompt evolution. Prerequisites: 04-system-prompts-and-instruction-design/system-prompt-anatomy.md, 04-system-prompts-and-instruction-design/dynamic-system-prompts.md
System Prompt Anatomy
An effective system prompt consists of six core components – role definition, context, behavioral constraints, tool instructions, output format, and examples – arranged to maximize instruction adherence within a limited token budget. Prerequisites: 01-foundations/how-llms-process-prompts.md, 01-foundations/context-window-mechanics.md