prompt composition

**Prompt composition** is the **systematic assembly of prompt components such as instructions, examples, retrieved context, and user query into a single coherent input** - composition quality strongly affects downstream model behavior. **What Is Prompt composition?** - **Definition**: Ordered construction of final prompt from modular context blocks. - **Component Types**: System directives, policy constraints, few-shot examples, retrieved evidence, and task request. - **Ordering Sensitivity**: Sequence and delimiter choices influence model attention and interpretation. - **Design Objective**: Maximize clarity, relevance, and instruction fidelity within token limits. **Why Prompt composition Matters** - **Answer Quality**: Poor composition can dilute instructions or bury critical context. - **Safety Integrity**: Clear trust boundaries are required between rules and untrusted input. - **Format Reliability**: Structured composition improves schema compliance and output consistency. - **Token Efficiency**: Good composition reduces redundancy and preserves space for high-value content. - **System Stability**: Repeatable composition patterns reduce run-to-run behavior variance. **How It Is Used in Practice** - **Layered Design**: Place high-priority instructions before examples and user-supplied data. - **Delimiter Discipline**: Explicitly fence untrusted context and document boundaries. - **Composition Testing**: Evaluate alternate orderings to optimize adherence and hallucination rates. Prompt composition is **a key prompt-engineering discipline for production systems** - deliberate assembly order and boundary design are essential for reliable, safe, and efficient model performance.